-
Adding a Mutation to a BatchWriter
After watching this video; you will be able to add a Mutation object to BatchWriter to insert data.
-
Applying Permissions to Users
After watching this video; you will be able to apply permissions to user accounts for system and table access and view the permissions a user has.
-
Authentication; Permissions; and Authorization
After watching this video; you will be able to describe how security is implemented in Accumulo through authentication; permissions; and authorization.
-
Basic Table Design
After watching this video; you will be able to design a basic table for Accumulo.
-
Cloning Tables
After watching this video; you will be able to clone tables.
-
Configuration (Part 1)
After watching this video; you will be able to edit the Accumulo configuration file and configure the memory settings.
-
Configuration (Part 2)
After watching this video; you will be able to build the native maps.
-
Configuration (Part 3)
After watching this video; you will be able to configure the cluster specifications and specify Accumulo setting values.
-
Configuring a Proxy
After watching this video; you will be able to describe the prerequisites for a proxy and configure it.
-
Configuring Classpath
After watching this video; you will be able to configure classpath to include the jars that Accumulo depends on.
-
Configuring Iterators Using the Client API
After watching this video; you will be able to set iterators using the Client API.
-
Configuring Iterators Using the Shell
After watching this video; you will be able to set iterators using the Shell.
-
Configuring Table Compaction Settings
After watching this video; you will be able to configure the table compaction settings to manage tablet compaction and the files within.
-
Connecting to Accumulo
After watching this video; you will be able to connect to Accumulo using a client.
-
Considerations for Writing Applications
After watching this video; you will be able to describe the considerations for writing applications.
-
Creating a Mutation
After watching this video; you will be able to create a Mutation object to write data.
-
Creating a Proxy Client
After watching this video; you will be able to create a proxy client.
-
Creating Constraints
After watching this video; you will be able to create constraints on tables to limit the data insert through a mutation.
-
Creating Indexes and Managing Tables
After watching this video; you will be able to create secondary indexes and manage tables in Accumulo.
-
Creating Indexes Partitioned by Documents
After watching this video; you will be able to create indexes partitioned by documents to group together documents for querying multiple terms.
-
Creating Indexes Partitioned by Term
After watching this video; you will be able to create indexes partitioned by terms to query data using a specific term.
-
Creating Locality Groups
After watching this video; you will be able to create locality groups using the Shell and Client API.
-
Creating Table Split Points
After watching this video; you will be able to add split points to create new tablets to improve performance.
-
Creating Tables
After watching this video; you will be able to create tables using the Shell.
-
Creating User Accounts
After watching this video; you will be able to create user accounts in the Accumulo shell.
-
Deleting Data
After watching this video; you will be able to delete data through a client.
-
Deleting Ranges of Data
After watching this video; you will be able to delete ranges of data from tablets.
-
Enabling Bloom Filters
After watching this video; you will be able to enable bloom filters.
-
Exporting Tables
After watching this video; you will be able to export tables to another Accumulo cluster.
-
File Formats
After watching this video; you will be able to describe the file format; Locality groups; bloom filters and caching.
-
Getting Started with the Shell
After watching this video; you will be able to start the shell and get help on shell commands.
-
Granting Authorizations to Users
After watching this video; you will be able to grant authorizations to user accounts.
-
Handling Errors During Insert Operations
After watching this video; you will be able to handle errors during insert operations with the BatchWriter.
-
Hardware Requirements
After watching this video; you will be able to identify the hardware requirements for Accumulo.
-
Indexing Data Types and Other Values
After watching this video; you will be able to index data types; IP addresses; dates; and domain names.
-
Initialize; Start; and Stop Accumulo
After watching this video; you will be able to initialize; start; and stop Accumulo; as well as check its status.
-
Inserting Data into Tables
After watching this video; you will be able to insert data into tables using the Shell.
-
Installation
After watching this video; you will be able to install Accumulo.
-
Installing Accumulo and Creating a Table
After watching this video; you will be able to install Accumulo and configure it; and create a table using the Shell.
-
Merging Tablets
After watching this video; you will be able to merge tablets.
-
Overview of Indexing
After watching this video; you will be able to describe indexing in Accumulo.
-
Performing Joins
After watching this video; you will be able to perform joins between Accumulo tables.
-
Pre-Installation Requirements
After watching this video; you will be able to identify the operating system requirements; Linux file system; and system services.
-
Preparation for Bulk Loading
After watching this video; you will be able to prepare information for bulk loading.
-
Querying a Document-partitioned Index
After watching this video; you will be able to query a document-partitioned index using a BatchScanner with an intersecting Iterator to find relevant pages in each partition.
-
Querying a Term-partition Index
After watching this video; you will be able to query a term-partitioned index for a particular term or a specific value for a field.
-
Reading; Writing; and Deleting Data
After watching this video; you will be able to read; write; delete; and update data using a client API in Accumulo.
-
Scanning a Table for Data
After watching this video; you will be able to scan a table for data using the scan command using the Shell.
-
Setting Column Visibilities on Tables
After watching this video; you will be able to set the column visibilities on tables to determine which key-values pairs are visible.
-
The Data Model
After watching this video; you will be able to describe the Accumulo data model.
-
The Read and Write API
After watching this video; you will be able to describe how the read and write API works to communicate with Accumulo.
-
Understanding the Additional Architecture Components
After watching this video; you will be able to describe the garbage collector; monitor; tracer and client.
-
Understanding the Master
After watching this video; you will be able to describe the main function of the Master.
-
Understanding the Tablet Server
After watching this video; you will be able to describe the read and write path and the Resource manager.
-
Understanding the Tablet Server Part 2
After watching this video; you will be able to describe the compactions and the write-ahead logs.
-
Updating Data
After watching this video; you will be able to update data through a client.
-
Using a BatchScanner
After watching this video; you will be able to use a BatchScanner to retrieve several rows simultaneously.
-
Using a Combiner with MapReduce
After watching this video; you will be able to use a combiner inside a MapReduce job for aggregation.
-
Using a Proxy Client
After watching this video; you will be able to use a proxy client by connecting; creating a table; and adding data.
-
Using a Scanner to Read Data
After watching this video; you will be able to use a scanner to read data through a client.
-
Using an Isolated Scanner
After watching this video; you will be able to use an isolated scanner to present an isolated view of rows when scanning.
-
Using Built-in Combiners
After watching this video; you will be able to use built-in combiners to combine all versions of a key-value pair.
-
Using Filters to Limited Data
After watching this video; you will be able to apply a filter policy when scanning data using different types of Filters.
-
Using MapReduce and Configuring Security
After watching this video; you will be able to use MapReduce and configure security in Accumulo.
-
Using MapReduce to Read Accumulo Tables
After watching this video; you will be able to use MapReduce to read information in Accumulo tables.
-
Using MapReduce to Write Accumulo Tables
After watching this video; you will be able to use a MapReduce job to write entries to Accumulo tables.
-
Using Range
After watching this video; you will be able to use Range to create a range covering all keys that match portions of a given key exactly.
-
Using TableOperation to Create Tables
After watching this video; you will be able to use the TableOperation object to create tables.
-
Using the ConditionalWriter
After watching this video; you will be able to use the ConditionalWriter to perform read-write operations on a row.
-
Using the VersionIterator
After watching this video; you will be able to use the VersionIterator to manage versioned data through timestamps within the Key.
-
Using Versioning
After watching this video; you will be able to use versioning to keep several versions of data on disk.
-
Writing Client APIs and Using Tables
After watching this video; you will be able to write client APIs and uses Accumulo Tables.
-
CAP Theorem
After watching this video; you will be able to describe CAP theorem and the choices made by Cassandra.
-
Cassandra Cluster Admin
After watching this video; you will be able to describe the statistics and management operations exposed via Java Management Extensions.
-
Configuring a Cluster with CCM
After watching this video; you will be able to install Apache Cassandra; Install CCM; and create a cluster.
-
Connecting to the Cluster
After watching this video; you will be able to connect to an Apache Cassandra cluster programmatically from Java.
-
CQL Shell
After watching this video; you will be able to execute CQL commands in Apache Cassandra using cqlsh.
-
Creating a Cluster with CCM
After watching this video; you will be able to create a cluster with Apache Cassandra Cluster Manager.
-
Deleting from Java
After watching this video; you will be able to delete from Apache Cassandra programmatically from Java.
-
Helenos
After watching this video; you will be able to describe the administration functions of Helenos in Apache Cassandra.
-
Important Files and Folders
After watching this video; you will be able to list the important files and folders in an Apache Cassandra installation.
-
Installing Cassandra
After watching this video; you will be able to install Apache Cassandra on Debian and RPM based systems.
-
Installing CCM
After watching this video; you will be able to install Apache Cassandra Cluster Manager.
-
Installing the Cassandra Jar
After watching this video; you will be able to set up a Java project with the Cassandra driver Jar file.
-
nodetool
After watching this video; you will be able to describe the functions of Apache Cassandra nodetool.
-
OpsCenter
After watching this video; you will be able to describe the administration functions of OpsCenter in Apache Cassandra.
-
Querying from Java
After watching this video; you will be able to query Apache Cassandra programmatically from Java.
-
SPM
After watching this video; you will be able to describe the monitoring functions of SPM in Apache Cassandra.
-
Starting a Node
After watching this video; you will be able to start an Apache Cassandra node.
-
Starting and Stopping an Instance
After watching this video; you will be able to start and stop a Cassandra instance.
-
The Big Data Challenge
After watching this video; you will be able to describe the big data challenge that Cassandra is designed to address.
-
The Cassandra Solution
After watching this video; you will be able to describe the solution to the problem that Cassandra represents.
-
The Partitioning Process
After watching this video; you will be able to describe the partitioning process in Apache Cassandra.
-
Troubleshooting CCM
After watching this video; you will be able to troubleshoot common issues with Apache Cassandra Cluster Manager.
-
Updating from Java
After watching this video; you will be able to update Apache Cassandra programmatically from Java.
-
Upgrading a Cluster with CCM
After watching this video; you will be able to upgrade a cluster with Apache Cassandra Cluster Manager.
-
Using CCM Commands
After watching this video; you will be able to use the command line to work with Apache Cassandra Cluster Manager.
-
Accessing HBase
After watching this video; you will be able to identify the various methods to access HBase through clients.
-
Accessing Other HBase Tables within a MapReduce Job
After watching this video; you will be able to access other HBase tables from within a MapReduce job by creating a Table instance in the setup method of Mapper.
-
Accessing the Web-Based Management Console
After watching this video; you will be able to access and navigate the web-based management console for HBase.
-
Adding a New Node
After watching this video; you will be able to add a new node to HBase.
-
Adding Data to a Table
After watching this video; you will be able to add data to an HBase table using the put shell command.
-
Adding Data Using the add() Option
After watching this video; you will be able to add data to an HBase table using the add() method in the Put instance.
-
Altering a Table's Properties
After watching this video; you will be able to alter an HBase table's properties.
-
Bulk Loading Data
After watching this video; you will be able to Use MapReduce to bulk load data directly into HBase file system by bypassing the HBase API.
-
Checking the Status of the HBase Instance
After watching this video; you will be able to use the HBaseAdmin API to check the status of the master server; connection instance; and the configuration used by the instance.
-
Compaction and Splits
After watching this video; you will be able to describe minor and major compaction and Region splitting.
-
Completing a Major Compaction Manually
After watching this video; you will be able to complete a major compaction using the HBase shell.
-
Creating a Put Class Instance
After watching this video; you will be able to create a Put class instance with a rowkey to store data in an HBase table.
-
Creating Tables Using the Client Java API
After watching this video; you will be able to create tables using the client Java API.
-
Creating Tables Using the Shell
After watching this video; you will be able to create a table using the shell in Hbase.
-
Data Replication
After watching this video; you will be able to describe how data replication is used in HBase.
-
Deleting Data From a Table
After watching this video; you will be able to delete data from an HBase table.
-
Deleting Tables
After watching this video; you will be able to disable and delete tables from HBase.
-
Designing Rowkeys for Tables
After watching this video; you will be able to design rowkeys for HBase tables.
-
Designing Tables
After watching this video; you will be able to identify the considerations and practices that go into designing an HBase table.
-
Disabling; Enabling; and Dropping a Table
After watching this video; you will be able to enable; disable; and drop a table using the shell in Hbase.
-
Exercise - Installing HBase
After watching this video; you will be able to install; configure; and secure Hbase.
-
Exporting and Restoring Snapshots
After watching this video; you will be able to export and restore a snapshot to another cluster.
-
Hadoop's MapReduce Integration with HBase
After watching this video; you will be able to describe MapReduce and how it is integrated with Hbase.
-
HBase Filesystems
After watching this video; you will be able to describe the filesystems used for HBase.
-
HBase Hardware Requirements
After watching this video; you will be able to identify the hardware requirements for HBase.
-
HBase Installation Modes
After watching this video; you will be able to describe the different HBase installation modes.
-
HBase Schema Overview
After watching this video; you will be able to describe the HBase schema.
-
HBase Software Requirements
After watching this video; you will be able to identify the software requirements for HBase.
-
HFiles and Regions
After watching this video; you will be able to describe the HFile and Region components and their functionalities in the HBase architecture.
-
Implementing Comparison Filters
After watching this video; you will be able to use comparison filters to limit the scan results using comparison operators and comparator instance.
-
Implementing Custom Filters
After watching this video; you will be able to use custom filters to extend or change the behavior of an existing filter to achieve a more fine-grained control over the scan results.
-
Implementing Utility Filters
After watching this video; you will be able to use utility filters that extend the FilterBase class to filter scan results.
-
Installing HBase in Fully Distributed Mode
After watching this video; you will be able to install HBase in fully distributed mode.
-
Installing HBase in Local Mode
After watching this video; you will be able to install HBase in local mode.
-
Listing the User Space Tables
After watching this video; you will be able to view a list of all the user space tables in HBase and the instance for the table.
-
Managing Data Using a Client API
After watching this video; you will be able to create an HBase table and insert; update; delete; and retrieve data using the Java Client API.
-
Managing HBase
After watching this video; you will be able to perform HBase cluster and node maintenance.
-
Merging Adjoining Regions
After watching this video; you will be able to merge regions in the same table using the Merge utility.
-
Monitoring HBase
After watching this video; you will be able to view metrics to monitor HBase.
-
Overview of HBase
After watching this video; you will be able to describe HBase and its features.
-
Performing a Backup on a Live Cluster
After watching this video; you will be able to perform a backup of HBase on a live cluster.
-
Performing a Full Shutdown Backup
After watching this video; you will be able to perform a full shutdown backup of HBase.
-
Performing a Restore
After watching this video; you will be able to restore HBase.
-
Performing a Rolling Restart
After watching this video; you will be able to perform a rolling restart on the entire cluster.
-
Retrieving Columns Using the Get Class
After watching this video; you will be able to use the addFamily() and addColumn () methods of the Get class.
-
Retrieving Specific Values From a Cell
After watching this video; you will be able to use the getValue() and getColumnLast() to retrieve specific values from a cell in an HBase table.
-
Retrieving Versions of Columns Using the Get Class
After watching this video; you will be able to use the setTimeRange(); setTimeStamp(); and setMaxVersions() methods of the Get class to retrieve versions of columns.
-
Scanning Rows Starting at a Specific Row or a Range
After watching this video; you will be able to use Scan() to read a table starting at a specific row or scan a range of rows from an HBase table.
-
Securing HBase
After watching this video; you will be able to secure HBase using authentication and authorization methods.
-
Splitting Map Tasks When Sourcing an HBase Table
After watching this video; you will be able to use the getSplits method of the TableInputFormatBase class to create custom splitters when using an HBase table as a data source.
-
Stopping and Decommissioning a RegionServer
After watching this video; you will be able to stop and decommission a RegionServer.
-
Taking a Snapshot
After watching this video; you will be able to take a snapshot.
-
The Write-Ahead Log and MemStore
After watching this video; you will be able to describe the functionality of the WAL and MemStore in an HBase architecture.
-
Time to Live and Deleted Cells
After watching this video; you will be able to determine which rows and cells to keep after deletion from a table.
-
Using a Snapshot to Clone a Table
After watching this video; you will be able to use a snapshot to clone a table and move it to another cluster.
-
Using Constructors to Narrow Search Results
After watching this video; you will be able to use different methods to further narrow search results from a Scan constructor.
-
Using Counters
After watching this video; you will be able to increment the counter using the incr shell command and get the counter value using get_counter.
-
Using getScanner() Method
After watching this video; you will be able to use the getScanner() methods to get the instance of a scan and browse the results.
-
Using HBase as a Data Sink for MapReduce Jobs
After watching this video; you will be able to use the TableOutPutFormat class to set up a table as an output to the MapReduce process using HBase as the data sink.
-
Using HBase as a Data Source for MapReduce Jobs
After watching this video; you will be able to use the TableInputFormat class to set up a table as an input to a MapReduce process using HBase as the data source.
-
Using List with the Get Class
After watching this video; you will be able to use List with the Get class to return a list of values in a batch.
-
Using Scan() to Read an Entire Table
After watching this video; you will be able to use Scan() to read an entire HBase table.
-
Using the Get Class
After watching this video; you will be able to use the Get class to read data from an HBase table.
-
Using the get() and has() Method
After watching this video; you will be able to use the get() and has() method to check the existence of a column in an HBase table.
-
Using the HBase Shell
After watching this video; you will be able to get started with using the HBase Shell.
-
Using the ResultScanner Class
After watching this video; you will be able to use the ResultScanner class to retrieve the rows from the scanner.
-
Using the scan and get Commands
After watching this video; you will be able to use the scan and get shell commands to retrieve data from an HBase table.
-
Using Timestamp with Put for Versioning
After watching this video; you will be able to specify a timestamp within a Put constructor to specify different versions.
-
Versions; DataTypes; and Joins
After watching this video; you will be able to design the schema to support versions; different datatypes; and joins.
-
Add New Topics
After watching this video; you will be able to add a new topic in Apache Kafka.
-
Adding and Removing a Broker
After watching this video; you will be able to add and remove a broker in Apache Kafka.
-
Async Producers
After watching this video; you will be able to describe the AsyncProducer API in Apache Kafka.
-
Balancing Data and Partitions for Performance
After watching this video; you will be able to move data and partitions in Apache Kafka for performance purposes.
-
Batching Messages
After watching this video; you will be able to batch messages in Apache Kafka.
-
Broker Configuration Settings
After watching this video; you will be able to configure Apache Kafka brokers.
-
Broker Discovery
After watching this video; you will be able to configure broker discovery in Apache Kafka.
-
Brokers
After watching this video; you will be able to describe Apache Kafka brokers.
-
Building a Custom Serializer
After watching this video; you will be able to build a custom serializer in Apache Kafka.
-
Configuring Compression
After watching this video; you will be able to configure compression in Apache Kafka.
-
Consumer Configuration Settings
After watching this video; you will be able to configure Apache Kafka consumers.
-
Consumers
After watching this video; you will be able to describe Apache Kafka consumers.
-
Creating a Producer and a Consumer
After watching this video; you will be able to configure a broker and create a producer and a consumer in Apache Kafka.
-
Deployment Overview
After watching this video; you will be able to describe the main options to deploy Apache Kafka.
-
Disk Throughput Tuning
After watching this video; you will be able to tune Linux systems disk throughput for Apache Kafka.
-
Hadoop Consumer API
After watching this video; you will be able to describe the Hadoop consumer API for reading from Apache Kafka.
-
Hardware Specifications
After watching this video; you will be able to describe common hardware and OS specifications and their impact in Apache Kafka.
-
High-Level Consumer API
After watching this video; you will be able to describe the high-level consumer API for reading from Apache Kafka.
-
JVM Tuning
After watching this video; you will be able to tune the Java VM for Apache Kafka.
-
Kafka Architecture
After watching this video; you will be able to Describe the architecture of Apache Kafka.
-
Kafka Offset Monitor
After watching this video; you will be able to monitor Apache Kafka using the offset monitor.
-
Kafka Test Suites
After watching this video; you will be able to use Apache Kafka test suites for testing.
-
Kafka Web Console
After watching this video; you will be able to monitor Apache Kafka using the Web Console.
-
Kernel Tuning
After watching this video; you will be able to tune the Linux kernel for Apache Kafka.
-
Keyed and Non-Keyed Messages
After watching this video; you will be able to specify keyed and non-keyed messages in Apache Kafka .
-
Message Acknowledgement
After watching this video; you will be able to configure message acknowledgement; or acking; in Apache Kafka.
-
Monitoring with Graphite
After watching this video; you will be able to monitor Apache Kafka using Graphite.
-
Monitoring with JMX
After watching this video; you will be able to monitor Apache Kafka using JMX.
-
Operating
After watching this video; you will be able to configure and manage Apache Kafka.
-
Partitions
After watching this video; you will be able to describe Apache Kafka topics.
-
Producer API
After watching this video; you will be able to describe the producer API in Apache Kafka.
-
Producer Configuration Settings
After watching this video; you will be able to configure Apache Kafka producers.
-
Producers
After watching this video; you will be able to describe Apache Kafka producers.
-
Puppet Deployment
After watching this video; you will be able to deploy Apache Kafka to Puppet.
-
Red Hat and CentOS Deployment
After watching this video; you will be able to deploy Apache Kafka to Red Hat and CentOS.
-
Replicas
After watching this video; you will be able to describe Apache Kafka replicas.
-
Scaling Consumers
After watching this video; you will be able to scale a consumer in Apache Kafka.
-
Scaling Producers
After watching this video; you will be able to scale a producer in Apache Kafka.
-
Simple Consumer API
After watching this video; you will be able to describe the simple consumer API for reading from Apache Kafka.
-
Sync Producers
After watching this video; you will be able to describe the SyncProducer API in Apache Kafka.
-
Topics
After watching this video; you will be able to describe Apache Kafka partitions.
-
Using Kafka Log Files
After watching this video; you will be able to monitor Apache Kafka using the log files.
-
What is Kafka?
After watching this video; you will be able to describe the function of Apache Kafka.
-
Building and Verifying
After watching this video; you will be able to describe data modeling points of view and modeling process flows.
-
Calculating Cube Size and Measures
After watching this video; you will be able to describe how to optimize cube.
-
Cardinality and Cubes
After watching this video; you will be able to describe basics of the concepts of cardinality and cubes in Apache Kylin.
-
Cardinality and Data
After watching this video; you will be able to explain cardinality and data and how it relates to Apache Kylin cubes.
-
Cube Build Steps
After watching this video; you will be able to identify Kylin as Apache incubator based on Calcite.
-
Cube Builds and Ordering
After watching this video; you will be able to list the measures currently supported in Kylin.
-
Cube Optimization
After watching this video; you will be able to describe how to build cube.
-
Designing Dimensions
After watching this video; you will be able to list and describe design dimensions.
-
Features of Kylin
After watching this video; you will be able to identify main components in Kylin Architecture.
-
How Cubes Work
After watching this video; you will be able to describe how cubes work.
-
How Kylin Processes Data
After watching this video; you will be able to identify how Apache Kylin processes data requests.
-
Introduction to Hadoop
After watching this video; you will be able to describe what Apache Kylin is; and the challenges and business needs for big data analysis.
-
Introduction to Kylin
After watching this video; you will be able to describe what Apache Kylin is; and the challenges and business needs for big data analysis.
-
Introduction to OLAP Cubes
After watching this video; you will be able to describe how cubes work.
-
Kylin Components
After watching this video; you will be able to identify main components in Kylin Architecture.
-
Kylin Cubes and Star Schema
After watching this video; you will be able to explain cubes and star schema in Kylin cube designing.
-
Kylin Performance
After watching this video; you will be able to explain the features that contribute to Kylin's performance.
-
Measures
After watching this video; you will be able to describe how to build and verify cube.
-
Other Features
After watching this video; you will be able to recognize additional Kylin features.
-
Pre-Join and Pre-Aggregation
After watching this video; you will be able to describe how storage engine works.
-
Star Schema
After watching this video; you will be able to define the star schema in Kylin cube designing.
-
Steps for Building a Cube
After watching this video; you will be able to define starting steps in Kylin cube designing.
-
Strategies for Kylin
After watching this video; you will be able to describe possible strategies for building an OLAP engine; its limitations as well as the Kylin approach to reaching goals .
-
Types of Dimensions
After watching this video; you will be able to define why order of dimensions is important step in cube design.
-
Why Kylin?
Why Kylin?
-
Analyzing with Frequent Pattern Mining
After watching this video; you will be able to perform analysis with frequent pattern mining.
-
Building Graphs
After watching this video; you will be able to build graphs.
-
Clustering with K-means
After watching this video; you will be able to perform clustering with K-means.
-
Clustering with Latent Dirichlet allocation (LDA)
After watching this video; you will be able to perform clustering with LDA.
-
Configuring Twitter Input Streams
After watching this video; you will be able to configure Twitter input streams.
-
Consuming Kafka Input Streams
After watching this video; you will be able to describe how Kafka input streams are consumed.
-
Creating Custom Accumulators
After watching this video; you will be able to create custom accumulators.
-
Creating DataFrames
After watching this video; you will be able to create dataframes.
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Creating Decision Trees
After watching this video; you will be able to create decision trees.
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Defining Lazy Evaluation
After watching this video; you will be able to define lazy evaluation as it relates to Spark.
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Deploying Applications
After watching this video; you will be able to deploy applications.
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Describing Data Types
After watching this video; you will be able to describe data types.
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Describing Receiver Reliability
After watching this video; you will be able to describe receiver reliability.
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Determining Memory Consumption
After watching this video; you will be able to determine memory consumption.
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Enabling and Configuring Checkpointing
After watching this video; you will be able to enable and configure checkpointing.
-
Examining Basic Statistics
After watching this video; you will be able to recall the basic statistics.
-
Examining Discretized Streams
After watching this video; you will be able to describe what a DStream is.
-
Examining RDD Lineage
After watching this video; you will be able to recall that RDD is an interface comprised of a set of partitions; list of dependencies; and functions to compute.
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Examining the Load and Save Functions
After watching this video; you will be able to describe the generic load and save functions.
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Examining the Property Graph
After watching this video; you will be able to describe the property graph.
-
Examining the Semantics of Fault Tolerance
After watching this video; you will be able to describe fault-tolerance semantics.
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Examining Vertex and Edge RDDs
After watching this video; you will be able to describe vertex and edge RDDs.
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Exploring Linear SVMs
After watching this video; you will be able to describe linear SVMs.
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Exploring the Graph Operators
After watching this video; you will be able to describe the graph operators.
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Exposing RDDs as Distributed Lists
After watching this video; you will be able to expose RDDs as distributed lists.
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Implementing Custom Input Streams
After watching this video; you will be able to implement custom input streams.
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Ingesting Flume Input Streams
After watching this video; you will be able to recall how Flume input streams are ingested.
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Ingesting TCP Socket Input Streams
After watching this video; you will be able to recall how TPC socket input streams are ingested.
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Installing SparkR
After watching this video; you will be able to install SparkR.
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Interoperating with DataFrames
After watching this video; you will be able to describe the k-means algorithm and how it's used for partitioning.
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Interoperating with RDDs
After watching this video; you will be able to interoperate with RDDs.
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Measuring Vertices with PageRank
After watching this video; you will be able to measure vertices with PageRank.
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Messaging with Pragel API
After watching this video; you will be able to perform messaging with Pragle API.
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Minimizing Memory Usage of Reduce Tasks
After watching this video; you will be able to use Spark's different shuffle operations to minimize memory usage of reduce tasks.
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Optimizing Broadcasts
After watching this video; you will be able to use broadcast functionality for optimization.
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Optimizing Representation through Partitioning
After watching this video; you will be able to optimize representation through partitioning.
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Perform Transformations on DStreams
After watching this video; you will be able to perform transformations on Dstreams.
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Performing Analytics with Neighborhood Aggregation
After watching this video; you will be able to perform analytics with Neighborhood aggregation.
-
Performing Batch Importing
After watching this video; you will be able to perform batch import on a Spark cluster.
-
Performing Join Operations
After watching this video; you will be able to perform join operations.
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Performing Logistic Regression
After watching this video; you will be able to perform logistic regression.
-
Performing Numeric Operations
After watching this video; you will be able to perform numeric operations on RDDs.
-
Performing Transform Operations
After watching this video; you will be able to perform transform operations.
-
Performing Window Operations
After watching this video; you will be able to perform Window operations.
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Persisting Stream Data in Memory
After watching this video; you will be able to persist stream data in memory.
-
Piping to External Applications
After watching this video; you will be able to pipe to external applications.
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Pre-partitioning RDDs
After watching this video; you will be able to pre-partition an RDD for performance.
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Reading and Writing Data in Hive Tables
After watching this video; you will be able to read and write data in Hive tables.
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Reading and Writing Data Using JDBC
After watching this video; you will be able to read and write data using JDBC.
-
Reading and Writing Parquet Files
After watching this video; you will be able to read and write Parquet files.
-
Reading File Input Streams
After watching this video; you will be able to describe how file input streams are read.
-
Receiving Akka Actor Input Streams
After watching this video; you will be able to recall how Akka Actor input streams are received.
-
Reducing Batch Processing Times
After watching this video; you will be able to reduce batch processing times.
-
Review of Spark Stack
After watching this video; you will be able to recall what is included in the Spark Stack.
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Running on a Cluster
After watching this video; you will be able to run SparkR on a cluster.
-
Running SparkR
After watching this video; you will be able to run SparkR.
-
Running Thrift JDBC/ODBC Server
After watching this video; you will be able to run the Thrift JDBC/OCBC server.
-
Setting Batch Intervals
After watching this video; you will be able to set the right batch interval.
-
Setting the Levels of Parallelism
After watching this video; you will be able to set the levels of parallelism for each operation.
-
Setting Up Kinesis Input Streams
After watching this video; you will be able to set up Kinesis input streams.
-
Storing RDDs in Serialized Form
After watching this video; you will be able to store RDDS in serialized form.
-
Tuning Data Structures
After watching this video; you will be able to tune data structures to reduce memory consumption.
-
Tuning Garbage Collection
After watching this video; you will be able to adjust garbage collection settings.
-
Tuning Memory Usage
After watching this video; you will be able to tune memory usage.
-
Tuning Spark
After watching this video; you will be able to show the different ways to tune up Spark for better performance.
-
Use MLlib
After watching this video; you will be able to use the algorithms and utilities in MLlib.
-
Using Collaborative Filtering with ALS
After watching this video; you will be able to use collaborative filtering with ALS.
-
Using Data Frames and SQL Operations
After watching this video; you will be able to use DataFrame and SQL operations on streaming data.
-
Using Existing R Packages
After watching this video; you will be able to use existing R packages.
-
Using JSON Dataset as a DataFrame
After watching this video; you will be able to use JSON Dataset as a DataFrame.
-
Using Learning Algorithms with MLlib
After watching this video; you will be able to use learning algorithms with Mllib.
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Using Naive Bayes
After watching this video; you will be able to use naïve bayes.
-
Using Output Operations on DStreams
After watching this video; you will be able to use output operations on Streams.
-
Using Parquet Files
After watching this video; you will be able to perform logistic regression to predict a binary response.
-
Using UpdateStateByKey Operations
After watching this video; you will be able to use the UpdateStateByKey operation.
-
Accumulators
After watching this video; you will be able to use accumulators in Spark operations.
-
Broadcast Variables
After watching this video; you will be able to use broadcast variables in a Spark operation.
-
Cluster Scheduling Options
After watching this video; you will be able to describe options for scheduling resources across applications in a Spark cluster.
-
Configuring Fair Scheduler Pool Properties
After watching this video; you will be able to configure fair sheduler pool properties for a Spark context within a cluster.
-
Configuring Fair Sharing at Application Level
After watching this video; you will be able to describe how to enable a fair scheduler for fair sharing within an application in a Spark cluster.
-
Creating Distributed Datasets
After watching this video; you will be able to load external datasets to create RDDs.
-
Creating Parallelized Collections
After watching this video; you will be able to introduce RDDs and create a parallelized collection to generate an RDD.
-
Deploying to a Cluster
After watching this video; you will be able to deploy a Spark application to a cluster.
-
Downloading and Installing Apache Spark
After watching this video; you will be able to download and install Apache Spark on Windows 8.1 Pro N.
-
Downloading and Installing Apache Spark on Mac OS X Yosemite
After watching this video; you will be able to download and install Apache Spark on Mac OS X Yosemite.
-
Getting Started with Spark GraphX
After watching this video; you will be able to use basic Spark GraphX to work with graphs in a Spark application.
-
Getting Started with Spark SQL
After watching this video; you will be able to use basic Spark SQL for data queries in a Spark application.
-
Initializing Spark
After watching this video; you will be able to create a SparkContext to initialize Apache Spark.
-
Linking an Application with Spark
After watching this video; you will be able to link an application to Spark.
-
Loading and Saving Spark Data
After watching this video; you will be able to use different formats for loading and saving Spark data.
-
Monitoring with Web UIs
After watching this video; you will be able to describe how to monitor a Spark application or cluster with web UIs.
-
Overview of Cluster Mode
After watching this video; you will be able to describe how Spark applications run in a cluster.
-
Overviewing Spark
After watching this video; you will be able to describe Apache Spark and the main components of a Spark application.
-
Passing Functions to Spark
After watching this video; you will be able to use anonymous function syntax and use static methods in a global singleton to pass functions to Spark.
-
RDD Actions
After watching this video; you will be able to describe some of the actions supported by Spark and use actions.
-
RDD Persistence
After watching this video; you will be able to persist Spark RDDs.
-
RDD Transformations
After watching this video; you will be able to distinguish transformations and actions; describe some of the transformations supported by Spark and use transformations.
-
Unit Testing
After watching this video; you will be able to unit test a Spark application.
-
Working with Key-Value Pairs
After watching this video; you will be able to work with key-value pairs .
-
Working with the Spark Shell
After watching this video; you will be able to use the Spark shell for analyzing data interactively.
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A Simple Trident Topology
After watching this video; you will be able to use Trident for a simple topology.
-
Apache Hadoop Introduction
After watching this video; you will be able to describe Apache Hadoop's use with Storm.
-
Apache Storm – Getting Started
After watching this video; you will be able to describe in a higher scope; Apache Storm and its characteristics.
-
Apache Storm Architecture Basics
After watching this video; you will be able to test your knowledge of Apache Storm and the components of the system.
-
Apache Storm Operations
After watching this video; you will be able to describe the different operation modes of Storm.
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Concept of Stream Groupings
After watching this video; you will be able to describe briefly stream groupings and their types.
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Configuration of Parallelism of a Topology
After watching this video; you will be able to configure the parallelism of spout and bolt components in a Storm topology.
-
Deploying ZooKeeper in Standalone Mode
After watching this video; you will be able to deploy a ZooKeeper server in standalone mode and test it with a ZooKeeper client connection.
-
Developing a Simple Topology
After watching this video; you will be able to identify Storm components and their functionality in the source code for an example Storm application.
-
DRPC Operating Modes and Topology Types
After watching this video; you will be able to describe DRPC modes of operation and topology types.
-
Exploring Micro-batching with Storm Core APIs
After watching this video; you will be able to describe some options for using Storm's Core APIs to implement micro-batching in a Storm Core topology.
-
Features and Benefits of Apache Storm
After watching this video; you will be able to describe why Apache Storm is used.
-
Installing ZooKeeper
After watching this video; you will be able to install and set up ZooKeeper on a development machine.
-
Introducing Distributed RPC
After watching this video; you will be able to describe distributed RPC model and how it is used with Apache Storm.
-
Introduction to Apache Kafka
After watching this video; you will be able to describe the general architecture of Apahce Kafka.
-
Introduction to Guaranteed Message Processing
After watching this video; you will be able to describe the Guaranteed Messaging Process.
-
Introduction to the Storm UI
After watching this video; you will be able to describe the Storm UI home page.
-
Introduction to Trident
After watching this video; you will be able to describe briefly what Trident is and how it's used.
-
Managing Topology State
After watching this video; you will be able to describe topology state management with Trident.
-
Operations with Trident
After watching this video; you will be able to describe several operations of Trident.
-
Overview on Fault Tolerance
After watching this video; you will be able to describe the fault-tolerant characteristics of Storm.
-
Running a Storm Application Using Maven
After watching this video; you will be able to use Maven to compile and run a Storm application.
-
Setting Up a Production Cluster
After watching this video; you will be able to demonstrate the process of setting up a production Storm cluster.
-
Setting Up a ZooKeeper Ensemble
After watching this video; you will be able to describe the process for setting up and deploying a ZooKeeper cluster.
-
Setting Up Apache Storm Development Environment
After watching this video; you will be able to describe the setup process for an Integrated Storm development environment.
-
Setting Up Standalone ZooKeeper
After watching this video; you will be able to describe the installation and setup process for ZooKeeper as a standalone server.
-
Spouts More Detail
After watching this video; you will be able to identify a spout and its use in Storm.
-
Streams Introduction
After watching this video; you will be able to identify streams and their use in Storm.
-
The Apache Storm Components
After watching this video; you will be able to describe the Apache Storm architecture.
-
The Kafka Data Model
After watching this video; you will be able to describe Kafka components and data model.
-
The Parallelism Concepts of a Topology
After watching this video; you will be able to describe the process of configuring the parallelism of a topology.
-
The Trident Data Model
After watching this video; you will be able to describe Trident's data model and its use.
-
Tuples and Bolts in More Detail
After watching this video; you will be able to identify a tuple and a bolt and their use in Storm.
-
Using Apache Storm Stream Grouping Concepts
After watching this video; you will be able to use stream groupings in a Storm topology.
-
Using Trident Spouts To Manage State
After watching this video; you will be able to describe the different types of Trident spouts available for implementing fault-tolerant Trident state management.
-
Using Trident State APIs
After watching this video; you will be able to describe the different Trident State APIs available for implementing fault-tolerant Trident state management.
-
Yet Another Resource Negotiator and Apache Storm
After watching this video; you will be able to describe how Apache Storm applications can be run on Hadoop YARN clusters to leverage YARN resource management.
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Analytics Team Members
The Big Data team is made up of various members; including data scientists; business analysts; and data analysts. These members all play a key role in the in the success of Big Data projects. In this video; Will Dailey demonstrates the roles of the team members who provide data analytics.
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Analytics Use Cases
The analytics provided by Big Data technologies are extensive; insightful; and advantageous for a wide range of industries. In this video; Will Dailey demonstrates the most commonly used Big Data analytics; and their applications.
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Apache Projects
In Big Data; you can have projects at various levels. Apache is responsible for organizing and managing these levels. In this video; Will Dailey introduces the basics of Apache projects.
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Apache Software Foundation
The Apache Software Foundation consists of a group of software developers who develop free open source software. In this video; Will Dailey demonstrates the Apache Software Foundation's approach to distributing open source software; as well as the pros and cons of open source software development.
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and Data Warehouses
Big Data can be managed using Data Warehousing which allows you to separate data based on its current status; for example from the production database to the archives. In this video; Will Dailey demonstrates how Data Warehousing can be used to manage Big Data.
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and RDBMs
The integration of Big Data computing systems and RDBMS allow for a broader and more efficient data management strategy than either system in isolation. In this video; Will Dailey discusses the benefits of integrating Big Data and RDBM systems; and outlines the integration process.
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Challenges
Big Data systems provide you with a high speed; low cost; high volume method of analytics computing. As its development continues; difficulties faced by the system are being rapidly overcome. In this video; Will Dailey demonstrates some of the challenges currently faced by Big Data.
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Engines
Big Data engines provide an innovative; user friendly platform for interacting with data. In this video; Will Dailey discusses key concepts relating to Big Data engines; and describes the functioning of a recommendation engine.
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Impacting Business
Big Data technology affects the way businesses perform strategic planning in terms of data and supercomputing; as well as the skills and training required by project teams. In this video; Will Dailey discusses the business culture and climate fostered by Big Data technology projects and planning.
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Impacting IT
As Big Data technologies develop and become more widely used; they will cause significant and lasting changes to IT organization. In this video; Will Dailey discusses the key outcomes of Big Data's impact on IT.
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Impacting the Globe
The ongoing development of Big Data technologies is resulting in vast improvements in data storage solutions and global connectivity. In this video; Will Dailey discusses the global impact and challenges of Big Data technologies.
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Opportunity
Big Data relates closely to the database marketplace as well as a range of future opportunities available to industries; governments; and research. In this video; Will Dailey discusses the database market segments; such as storage for data; servers for databases; business intelligence; and analytics. He also covers future trends in market value; growth; and consolidation.
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Business Case for
Using a Hadoop solution with Big Data allows you to manage data accurately; swiftly; and at less cost than Data Warehousing. In this video; Will Dailey demonstrates the benefits of replacing Data Warehousing systems with Hadoop when working with Big Data.
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Business Team Members
Big Data accelerates the need for knowledge of technology in the executive suite. These days; executives; data analysts; and business analysts need to demonstrate a more hands-on approach and possess more than just overview knowledge of technology. In this video; Will Dailey demonstrates the role of data analysts and business analysts in Big Data.
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Challenges of Security and Privacy
Big Data systems provide you with a high speed; low cost; and high volume method of analytics computing. However; with the advantages it brings; there are still challenges associated with the use of Big Data computing; particularly in relation to security and privacy. In this video; Will Dailey demonstrates the security and privacy challenges faced by Big Data systems.
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Cluster Team Members
Big Data engineers and operators are responsible for the management and operation of Hadoop clusters; and are required to have extensive abilities applicable to the supercomputing platform. In this video; Will Dailey discusses the responsibilities and requirements of the Hadoop cluster team members.
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Data Analytics
Big Data's continuously improving technologies provide high-end tools for the analysis and presentation of raw data. In this video; Will Dailey discusses the techniques and tools used in the processing of Big Data.
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Data Center Projects
The introduction of Big Data computing platforms into data centers will result in technological and cost benefits outweighing those of the mainframe systems. In this video; Will Dailey discusses the benefits and considerations of converting mainframe data management systems to Big Data computing platforms.
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Data Center Teams
Of the many lessons learned when implementing a Big Data solution; one of the most important is how to develop a good team. In this video; Will Dailey demonstrates how to develop a successful Big Data team; the importance of a mission statement; migrating from an Enterprise to a Global Architecture; and mastering the Big Data ecosystem.
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Data Solutions Team Members
Big Data teams consist of various architectural members; including platform engineers/global architects and data architects/data wranglers. These members are vital to the team and contribute significantly towards data management and development. In this video; Will Dailey demonstrates the roles of the architectural team members on the Big Data team.
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Defining
Big Data is about data on a global scale; and it's important to be familiar with its definitions. In this video; Will Dailey defines some of the key terms used in characterizing Big Data.
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Design Principles of Hadoop
Big Data supports the Hadoop framework which allows you to compute large amounts of data while meeting the requirements for supercomputing. Hadoop supports five design principles – dumb hardware and smart software; share nothing; move processing; not data; embrace failure; and build applications; not infrastructure. In this video; Will Dailey provides an overview of Hadoop and its design principles.
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DIY Supercomputing
In Big Data; there are different options within the datacenter for developing a DIY supercomputing platform. In this video; Will Dailey discusses the key principles for successful global architecting of a supercomputing platform.
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Emerging Technologies
Big Data is the new global biosphere for computing. In this video; Will Dailey discusses the emerging technologies that form the foundation of Big Data.
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Functional View of Hadoop
Hadoop is an open-source software project used to store and distribute Big Data. It is designed for fault tolerance and has high availability and scalability in a supercomputing environment. In this video; Will Bailey demonstrates Hadoop technology; including its advantages and its functional components.
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Global Increasing Digital Volume
The astronomical growth in digital data is one of the prime drivers for Big Data. In this video; Will Dailey discusses improvements in quality and service levels while reducing costs in different industries.
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Hadoop in the Clouds
Big Data Cloud computing provides you with distinct performance advantages; including elastic computing and fast implementation of configuration changes. In this video; Will Dailey demonstrates the benefits of Big Data Cloud computing and discusses considerations related to its use.
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HDFS in Action
In Big Data; Hadoop Distributed File System (HDFS) simplifies the distribution of large data files across multiple data nodes. In the video; Will Dailey demonstrates what HDFS is; how it works; and its benefits.
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Key Terms for Data
Some key terms used in Big Data are the Big Data ecosystem; a Distributed Computing Environment; supercomputing platform; and Hadoop cluster. In this video; Will Dailey discusses the key terms used in Big Data.
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MapReduce in Action
In Big Data; Hadoop's MapReduce is a powerful tool for processing and generating large sets of data. It consists of two components –Mapper and Reducer. In this video; Will Dailey discusses the benefits of MapReduce; as well as how its two components work together.
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NoSQL Databases
NoSQL consists of a large range of databases; not limited only to SQL; which store Big Data. These databases are faster; more powerful; and simpler to use than traditional databases. In this video; Will Bailey discusses the use of NoSQL for storing Big Data.
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Other Apache Projects
There are several Apache projects which provide an open source framework for storing and processing Big Data. In this video; Will Dailey discusses the Accumulo; Avro; and the Oozie Apache projects.
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Other Open Source Projects
With Big Data; there are many open source projects. In addition to Apache; there are projects such as MongoDB and Cascading. In this video; Will Dailey discusses the differences between the various available open source projects.
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Planning for
Planning for Big Data requires careful examination of all the issues and a willingness to experiment. In this video; Will Dailey demonstrates various planning approaches; such as experimentation; going for it; and the plan-budget-build model; which includes the Agile software development strategy.
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Sizing
Big Data requires us to extend our terminology for the sizing of data. Most of these are unfamiliar; so it is important to learn them correctly. In this video; Will Dailey presents a list of key terminology that should be used in regard to the sizing of data.
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Spark in Action
Apache Spark allows for robust; versatile; and fast Big Data analytics computing. In this video; Will Dailey discusses the key properties and benefits of the Spark computing framework in relation to Big Data.
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The Big Companies
The rise of Big Data technologies has resulted in an increased interest from the world's leading tech companies in the development of Big Data products and services. In this video; Will Dailey provides an overview of these leading tech companies in relation to Big Data.
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The Stack
In Big Data; supercomputing platforms are built on a complex stack of technology. Mappings are required to understand and manage it. In this video; Will Dailey demonstrates mapping to understand the workings of Big Data architecture.
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The Biggest Wave Yet
Big Data is said to be the biggest technology wave yet. It provides huge amounts of data so that organizations; such as government and market research companies; can analyze and then interact with us differently. In this video; Will Dailey puts Big Data into the perspective of technology changes over the past 70 years.
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The Distro Companies
In Big Data; Distro companies are distribution companies that support unique distributions of Hadoop; which is open source software for handling Big Data. In this video; Will Dailey discusses four Distro companies and their go-to market strategies.
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The Original Key Contributors
The original key contributors of Big Data are Google; Yahoo; and Doug Cutting. In this video; Will Dailey discusses the main contributions to the development of Big Data technology.
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YARN in Action
In Big Data; YARN is a parallel processing framework that allows for the processing of large amounts of data over several computer nodes. In this video; Will Dailey demonstrates the key attributes of YARN as well as how it works.
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Deciding Where to Start
After watching this video; you will be able to decide where to start.
-
Designing Governance Strategy
After watching this video; you will be able to design a governance strategy.
-
Establishing Economic Value
After watching this video; you will be able to establish economic value.
-
Examining Culture Clash
After watching this video; you will be able to describe the big data culture clash.
-
Examining Security
After watching this video; you will be able to identify security concerns.
-
Exploring Big Data Products
After watching this video; you will be able to recall how big data products are economic engines.
-
Exploring Incubators
After watching this video; you will be able to describe what incubators are and how they can improve companies.
-
Exploring Stewardship
After watching this video; you will be able to identify stewardship issues.
-
Impacting Human Resources
After watching this video; you will be able to describe how this will impact the human resources department.
-
Obtaining Big Data Skills
After watching this video; you will be able to compare building versus buying approaches to big data skill.
-
Building a Big Data Team
After watching this video; you will be able to recall the skill sets that individuals should have to make up the big data team.
-
Building Cloud Infrastructures
After watching this video; you will be able to recall some of the options when building a cloud based big data infrastructure.
-
Building On-site Infrastructures
After watching this video; you will be able to compare the pros and cons of building an in house big data infrastructure.
-
Communicating Actionable Insights
After watching this video; you will be able to compare the different types of data visualization that can be done with the analytics.
-
Deciding on Big Data Needs
After watching this video; you will be able to recall what factors are important when considering a big data infrastructure.
-
Defining Big Data
After watching this video; you will be able to describe big data and the three v's.
-
Examining Analytics
After watching this video; you will be able to recall the types of analytics that can be done with the data.
-
Potential and Pitfalls of Big Data
After watching this video; you will be able to describe the risks involved when considering using big data as a solution.
-
Real World Examples
After watching this video; you will be able to recall how large companies have used big data analytics effectively.
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Software for Big Data
After watching this video; you will be able to describe the different software options that are available for big data analytics.
-
Addressing Data Discovery
After watching this video; you will be able to recall how technology assisted reviews can reduce workload.
-
Examining Privacy Concerns
After watching this video; you will be able to describe the privacy concerns over big data.
-
Examining Responsibility with Predictions
After watching this video; you will be able to describe the dilemma of predictive capabilities.
-
Examining Transparency
After watching this video; you will be able to recall how transparency helps companies improve their customer relationships.
-
Exploring Data Collection
After watching this video; you will be able to describe the areas of concern to address before data collection is done.
-
Identifying Challenges of Laws and Regulations
After watching this video; you will be able to recall how constantly changing laws and regulations are a challenge.
-
Ignoring Social Media
After watching this video; you will be able to recall the legal concerns over using social media sites to recruit potential employees.
-
Monetizing Big Data
After watching this video; you will be able to describe the goals and the hazards of data monetization.
-
Protecting Data
After watching this video; you will be able to recall some of the results that can occur when data is not properly protected.
-
Using Data Ethically
After watching this video; you will be able to describe why the governance of the tools used to collect and analyze the data is important.
-
Describing Pitfalls
After watching this video; you will be able to describe different ways that companies can misuse the new technology.
-
Examining Big Data and Marketing
After watching this video; you will be able to describe how big data is used in marketing.
-
Examining New Techniques
After watching this video; you will be able to describe technique changes in split testing and cross-channel marketing.
-
Examining Purchasing Habits
After watching this video; you will be able to describe the value of repeat shoppers.
-
Examining the Mobile Effect
After watching this video; you will be able to describe how the mobile effect has changed marketing.
-
Exploring Datafied Consumer Behavior
After watching this video; you will be able to describe how big data has datafied consumer behavior.
-
Exploring Market Research Methods
After watching this video; you will be able to describe the new market research methods.
-
Exploring Technology Trends
After watching this video; you will be able to describe how marking strategies need to change with technology.
-
Exploring the Path to Purchase
After watching this video; you will be able to describe how technology has changed the way that product discovery and product research is done.
-
Using User Profiling
After watching this video; you will be able to describe how using user profiling can assist in advertising.
-
Defining Data Science
After watching this video; you will be able to compare data science to big data.
-
Defining The Internet of Things
After watching this video; you will be able to describe the term The Internet of Things.
-
Examining Algorithms
After watching this video; you will be able to describe the different algorithms behind the systems we know.
-
Examining Barriers to Adoptions
After watching this video; you will be able to describe the most common barrier to technology adaptation in the work place.
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Examining Big Data Awareness
After watching this video; you will be able to describe how big data entered into the public consciousness.
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Examining Case Studies
After watching this video; you will be able to recall how different companies embraced the big data movement.
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Examining Software
After watching this video; you will be able to recall some different software implementations for big sales data.
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Examining Technology Accelerations
After watching this video; you will be able to identify different technologies that are accelerating the sales world.
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Finding Leads
After watching this video; you will be able to find leads using big data.
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Building the Right Infrastructure
After watching this video; you will be able to compare scaling up to scaling out.
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Examining Big Data Challenges
After watching this video; you will be able to describe the four main challenges of big data.
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Examining Leadership Concerns
After watching this video; you will be able to describe why big data is a leadership problem.
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Exploring Analytical Modes
After watching this video; you will be able to identify examples of analytical modes.
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Exploring Examples
After watching this video; you will be able to recall some of the top ten companies that are using big data solutions.
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Integrating Data Sources
After watching this video; you will be able to integrate from multiple data sources.
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Managing Risk and Governance Issues
After watching this video; you will be able to manage risk and governance issues.
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Measuring Data Value
After watching this video; you will be able to identify the value of big data.
-
Securing Funds
After watching this video; you will be able to secure funding for big data initiatives.
-
DOM-based XSS
After watching this video; you will be able to identify what DOM-based XSS is and illustrate the form an attack might take in JavaScript.
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Basics of Hadoop
Apache Hadoop is an open source platform that is used for the possessing and storage of Big Data applications. In this video; Bob Hendry will discuss about the basics of Hadoop.
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Chaining MapReduce Jobs
Job chaining is a specialized form of process control that automates an execution sequence made up of a number of MapReduce jobs. In this video; Bob Hendry discusses how to chain MapReduce jobs.
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Clustering
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the benefits of clustering.
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Creating Job Components
Hadoop MapReduce is a Java-based; open-source platform for processing Big Data in real time. In this video; Bob Hendry explains the components of a Hadoop MapReduce job.
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Executing Commands
The Hadoop Distributed File System (HDFS) provides high-throughput access to large amounts of data. In this video; Bob Hendry discusses how MapReduce jobs are run; executed; and deployed in different ways across the Hadoop framework.
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Executing Hadoop MapReduce Jobs
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry discusses how to write a MapReduce job.
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File Operations within the HDFS
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss how to perform file operations within the Hadoop Distributed File System (HDFS).
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Hadoop Distributions
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss third-party distribution.
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Hadoop Ecosystem
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will define and discuss the Hadoop ecosystem.
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Hadoop Installation Considerations
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry explains how to install Apache Hadoop.
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Hadoop MapReduce and Java
Java functions as the Hadoop MapReduce implementation language. In this video; Bob Hendry discusses how to use Java and additional Java Archive (JAR) files to write Hadoop MapReduce jobs.
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How Data is Processed; Persisted; and Read on the HDFS
A unique feature of Hadoop is that instead of data moving to data to a processing location; processing is moved to the data. In this video; Bob Hendry explains how Big Data is processed; persisted and read in the Hadoop Distributed File System (HDFS).
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Interacting with the HDFS
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss how to interact with the Hadoop Distributed File System (HDFS).
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Introduction to MRUnit
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry explains Apache MRUnit.
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Introduction to Server Farms
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the deployment of Hadoop in a server farm.
-
Introduction to the Hadoop Distributed File System
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the Hadoop Distributed File System (HDFS) and its architecture.
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Introduction to the MapReduce Life Cycle
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss how Hadoop uses MapReduce and explain the MapReduce life cycle.
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Job Flow Progress and Monitoring
The Hadoop MapReduce framework supports data intensive distributed applications. In this video; Bob Hendry discusses Hadoop MapReduce job flow progress and monitoring.
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Killing MapReduce Jobs
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry explains how to manage a MapReduce job.
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Loading and Storing Data in Pig
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry discusses various Pig commands.
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Overview of Hadoop; Storage; MapReduce; Pig; and Hive
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the components of Hadoop.
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Sources of Big Data
Apache Hadoop is an open source platform that manages large sources of data. In this video; Bob Hendry will discuss how new sources of data compare with traditional; and will analyze the effect this new data has had on e-commerce.
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Subdividing Data
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss how data is formatted and subdivided prior to a MapReduce job.
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Testing your Hadoop Installation
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss how to test and verify an Apache Hadoop installation.
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The Four Vs of Big Data
Apache Hadoop is an open source platform used to help manage the essential four Vs of Big Data. In this video; Bob Hendry will discuss the four Vs of Big Data.
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The MapReduce Principles; Mappers; and Reducers
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the principles of MapReduce and general mapping issues.
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The Role of YARN
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the YARN (Yet Another Resource Negotiator).
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Types of NoSQL Databases
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the four types of NoSQL databases and also their usage.
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Understanding Data
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the three types of data managed by Hadoop.
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Understanding High-Level Processing
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry explains the processing of the MapReduce jobs.
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Understanding Hive File Formats
Apache Hive enables Hadoop to operate as a data warehouse by superimposing a table on data in the Hadoop Distributed File System (HDFS). In this video; Bob Hendry reveals tools and file formats supported by Hive for creating tables.
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Understanding MapReduce with a Conceptual Example
Hadoop MapReduce can be used to accelerate and scale various data analysis tasks. In this video; Bob Hendry explains how Hadoop uses MapReduce to process data.
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Understanding Persistence and Types of Big Data
Persistence describes the state of data that outlives the process that created it. In this video; Bob Hendry explains persistence; and the specifics of reading and writing data in the Hadoop Distributed File System (HDFS).
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Understanding the MapReduce Data Flow
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss MapReduce data flow.
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Users of Hadoop
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the different players using Hadoop.
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Using a Mini MapReduce and HDFS Cluster
Unit testing MapReduce job is a complex process in the Hadoop and MapReduce framework. In this video; Bob Hendry discusses the technique of testing MapReduce job by creating a mini MapReduce and Hadoop Distributed File System (HDFS) cluster.
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Using HiveQL to Write Queries
Hadoop MapReduce provides a number of tools for building distributed systems. In this video; Bob Hendry explains the basics of Apache Hive and HiveQL.
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Using JUnit
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry discusses JUnit testing tool for troubleshooting the MapReduce jobs.
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Using MapReduce with Pig and Hive
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss how to use Pig and Hive in conjunction with Hadoop.
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Using Pig Latin to Communicate with Hadoop
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry explains the benefits of Apache Pig and how it is used with Hadoop.
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Using the Hadoop LocalJobRunner
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video Bob Hendry discusses the basics of Hadoop LocalJobRunner.
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Viewing Job Status and Log Files
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry demonstrates monitoring the status of a MapReduce job.
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Working with Custom Hive Data Types
HiveQL is an SQL-like language for handling data warehousing using Apache Hive over Hadoop. In this video; Bob Hendry explains the use of custom Hive data types.
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Writing Pig Scripts
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry demonstrates using Pig Latin script.
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Your First Hadoop Cluster
Apache Hadoop is an open source platform for managing Big Data. In this video; Bob Hendry will discuss the architecture of a Hadoop cluster.
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MapReduce; Hive; and Pig
Hadoop MapReduce is a Java-based programmatic algorithm for managing Big Data. In this video; Bob Hendry explains three components of Hadoop - MapReduce; Hive; and Pig.
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Admin Scripts in Action
After watching this video; you will be able to write custom scripts to assist with administrative tasks.
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Architecture of Sqoop
After watching this video; you will be able to describe Sqoop's architecture.
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Avro Serialization
After watching this video; you will be able to recall that Avro can be used as both a sink and a source.
-
Best Practices for Pseudo-Mode
After watching this video; you will be able to recall some best practices for pseudo-mode implementation.
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Bucketing Hive Data
After watching this video; you will be able to create Hive buckets.
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Class Paths In Action
After watching this video; you will be able to troubleshoot classpath errors.
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Cleaning with Functions
After watching this video; you will be able to use Hive to create tables outside the warehouse.
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Cleaning with Regular Expressions
After watching this video; you will be able to use Pig to sample data.
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Comparing NoSQL to RDBMS
After watching this video; you will be able to describe key attributes of NoSQL databases.
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Config Files in Action
After watching this video; you will be able to create complex configuration files.
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Configuration Changes
After watching this video; you will be able to describe the best practices for changing configuration files.
-
Configuring Hadoop Environment
After watching this video; you will be able to configure Hadoop environmental variables.
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Configuring HDFS
After watching this video; you will be able to configure Hadoop HDFS.
-
Configuring Hue
After watching this video; you will be able to configure Hue and Hue users.
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Configuring Hue with MySQL
After watching this video; you will be able to configure Hue with My SQL.
-
Configuring Oozie
After watching this video; you will be able to configure Oozie.
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Configuring Oozie with MySQL
After watching this video; you will be able to configure Oozie to use MySQL.
-
Configuring YARN and MapReduce
After watching this video; you will be able to configure Hadoop YARN and MapReduce.
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Continuing to Map Big Data
After watching this video; you will be able to recall three major functions of data analytics.
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Correcting a Database Connect Failure
After watching this video; you will be able to use the log files to identify common Sqoop errors and their resolutions.
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Create a Hive UDF
After watching this video; you will be able to create a user defined function for Hive.
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Creating a Development Environment
After watching this video; you will be able to configure the start-up shell and yum repositories.
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Creating a Pig UDF
After watching this video; you will be able to create a user defined function for Pig.
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Creating a View in Hive
After watching this video; you will be able to use Hive to perform joins.
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Creating an Oozie Workflow
After watching this video; you will be able to create an Oozie workflow.
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Creating Flume Agents
After watching this video; you will be able to describe a flume agent in detail.
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Debugging with Pig Explain
After watching this video; you will be able to use explain in a Pig script.
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Defining Flume
After watching this video; you will be able to recall some best practices for the Agent Conf files.
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Defining Flume Data
After watching this video; you will be able to describe the three key attributes of Flume.
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Describing a Flume Agent
After watching this video; you will be able to create a Flume agent.
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Describing HDFS Data Blocks
After watching this video; you will be able to configure the replication of data blocks.
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Describing HDFS URI
After watching this video; you will be able to configure the default file system scheme and authority.
-
Describing the Backup Node
After watching this video; you will be able to describe the purpose of the Backup Node and describe its standard operations.
-
Describing the Checkpoint Node
After watching this video; you will be able to describe the purpose of the CheckPoint Node.
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Describing the DataNode
After watching this video; you will be able to recall how the DataNode maintains data integrity.
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Describing the NameNode
After watching this video; you will be able to describe the functions of the NameNode.
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Design Principles for Hadoop
After watching this video; you will be able to recall the design principles of Hadoop.
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Ecosystem for Hadoop
After watching this video; you will be able to describe supercomputing.
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Enabling the Oozie Examples
After watching this video; you will be able to enable the Oozie examples.
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Explaining Parallel Processing
After watching this video; you will be able to describe parallel processing in the context of supercomputing.
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Exploring Hadoop Classpath
After watching this video; you will be able to set up the classpath and test WordCount.
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Exporting Data from Hive
After watching this video; you will be able to use Sqoop to export data from Hive.
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Exporting Datetime Stamps
After watching this video; you will be able to export datetime stamps from Hive into MySQL.
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Exporting into HBase
After watching this video; you will be able to perform a Sqoop import from MySQL into Hbase.
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File Channel to HDFS
After watching this video; you will be able to describe what is happening during a file roll.
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First Flume Agent
After watching this video; you will be able to use a flume agent to load data into HDFS.
-
First Pig Script
After watching this video; you will be able to write a Pig script.
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First Use of HDFS
After watching this video; you will be able to recall the structure of the HDFS command.
-
First Use of WordCount
After watching this video; you will be able to run WordCount.
-
Flume Channels
After watching this video; you will be able to describe Flume channels.
-
Flume Sinks
After watching this video; you will be able to identify popular sinks.
-
Flume Sources
After watching this video; you will be able to identify popular sources.
-
Flume Timestamps
After watching this video; you will be able to describe interceptors.
-
Flume Troubleshooting
After watching this video; you will be able to recall some common reasons for Flume failures.
-
Hadoop Distributed File System
After watching this video; you will be able to describe the components of the Hadoop Distributed File System (HDFS).
-
Hadoop Streaming
After watching this video; you will be able to identify the concept of streaming for MapReduce.
-
Hadoop Web UIs
After watching this video; you will be able to recall the ports of the NameNode and Resource Manager Web Uis.
-
HDFS Administration
After watching this video; you will be able to perform common administration functions.
-
HDFS Command Categories
After watching this video; you will be able to recall the syntax of the file system shell commands.
-
HDFS Commands for Managing Data Files
After watching this video; you will be able to use shell commands to manage files.
-
HDFS Commands for Managing Web Logs
After watching this video; you will be able to use shell commands to provide information about the file system.
-
HDFS Configuration
After watching this video; you will be able to configure parameters for NameNode and DataNode.
-
Hive CREATE Statements
After watching this video; you will be able to use basic SQL commands in Hive.
-
Hive Data Types
After watching this video; you will be able to recall the unique delimiter that Hive uses.
-
Hive Operators
After watching this video; you will be able to describe the different operators in Hive.
-
Hive SELECT Statements
After watching this video; you will be able to use SELECT statements in Hive.
-
Hive SQL Statements
After watching this video; you will be able to use more complex Hive SQL.
-
Hive Troubleshooting
After watching this video; you will be able to recall the standard error code ranges and what they mean.
-
Hive User-Defined Functions
After watching this video; you will be able to recall some best practices for user defined functions.
-
Hue in action
After watching this video; you will be able to use the Hue File Browser and Job Scheduler.
-
Importing Data into Hive
After watching this video; you will be able to use Sqoop to import data into Hive.
-
Importing Datetime Stamps
After watching this video; you will be able to use casting to import datetime stamps into Hive.
-
Installing and Configuring Pig
After watching this video; you will be able to install and configure Pig.
-
Installing Flume
After watching this video; you will be able to install and configure Flume.
-
Installing Hadoop
After watching this video; you will be able to use Cloudera Manager to install Hadoop.
-
Installing HBase
After watching this video; you will be able to install and configure Hbase.
-
Installing Hive
After watching this video; you will be able to install and configure Hive.
-
Installing HiveServer2
After watching this video; you will be able to install and configure HiveServer2.
-
Installing Hue
After watching this video; you will be able to install Hue.
-
Installing Java
After watching this video; you will be able to install the Java Developers Kit.
-
Installing metastore
After watching this video; you will be able to install and configure metastore.
-
Installing MySQL
After watching this video; you will be able to install MySQL.
-
Installing Oozie
After watching this video; you will be able to install Oozie.
-
Installing Sqoop
After watching this video; you will be able to install Sqoop.
-
Installing WebHCat
After watching this video; you will be able to install and configure WebHCat.
-
Introducing cURL
After watching this video; you will be able to recall some of the protocols cURL supports.
-
Introducing Data Analytics
After watching this video; you will be able to describe data analytics.
-
Introducing Data Factory Components
After watching this video; you will be able to identify the data factory components.
-
Introducing Data Refinery Components
After watching this video; you will be able to identify the data refinery components.
-
Introducing Data Repository Components
After watching this video; you will be able to identify the data repository components.
-
Introducing Hadoop YARN
After watching this video; you will be able to describe Hadoop YARN.
-
Introducing HBase and ZooKeeper
After watching this video; you will be able to describe the roles of HBase and ZooKeeper.
-
Introducing HDFS Daemons
After watching this video; you will be able to describe the four main HDFS daemons.
-
Introducing MapReduce
After watching this video; you will be able to define MapReduce and describe its relations to YARN.
-
Introducing WordCount
After watching this video; you will be able to recall the importance of the output directory.
-
Joining and Viewing in Hive
After watching this video; you will be able to recall what types of joins Hive can support.
-
Key-Value Pairs
After watching this video; you will be able to define the principle concepts of key-value pairs and list the rules for key-value pairs.
-
Loading Data into MySQL Tables
After watching this video; you will be able to create MySQL tables and load data.
-
Loading SQL Data Tables
After watching this video; you will be able to create a table and load data into MySQL.
-
Logging to the Flume Log File
After watching this video; you will be able to use the logger to troubleshoot Flume agents.
-
Mapping the Big Data Stack
After watching this video; you will be able to describe the components of the Big Data stack.
-
MapReduce and Key-Value Pairs
After watching this video; you will be able to describe how MapReduce transforms key-value pairs.
-
MapReduce Step-by-Step
After watching this video; you will be able to match the phases of MapReduce to their definitions.
-
Mastering the
After watching this video; you will be able to describe the reasons for the complexities of the Hadoop Ecosystem.
-
Multi-flow Flume Agents
After watching this video; you will be able to configure Flume agents for multiflow.
-
Multiple Flume Agents
After watching this video; you will be able to create multiple-hop Flume agents.
-
Multiple Sinks with Flume
After watching this video; you will be able to compare replicating to multiplexing.
-
Multiple Sources with Flume
After watching this video; you will be able to describe multifunction Flume agents.
-
Multi-sink Flume Agents
After watching this video; you will be able to create a Flume agent for multiple data sinks.
-
NameNode and Resource Manager Web UIs
After watching this video; you will be able to use the NameNode and Resource Manager Web Uis.
-
NameNode in Detail
After watching this video; you will be able to recall how the NameNode operates.
-
Oozie Workflows
After watching this video; you will be able to describe Oozie workflows.
-
Overview of Bucketing Hive Data
After watching this video; you will be able to recall how buckets are used to improve performance.
-
Overview of Exporting with Sqoop
After watching this video; you will be able to recall what concerns the developers should be aware of.
-
Overview of Hive Configuration
After watching this video; you will be able to describe the configuration files.
-
Overview of Importing with Sqoop
After watching this video; you will be able to recall why it's important for the primary key to be numeric.
-
Overview of Partitioning Hive Data
After watching this video; you will be able to recall that a Hive partition schema must be created before loading the data.
-
Overview of Pig Configuration
After watching this video; you will be able to recall the minimal edits needed to be made to the configuration file.
-
Overview of Sqoop Configuration
After watching this video; you will be able to recall the dependancies for Sqoop installation.
-
Overviewing Hive
After watching this video; you will be able to recall the key attributes of Hive.
-
Overviewing Hue
After watching this video; you will be able to describe Hue.
-
Overviewing MySQL
After watching this video; you will be able to describe MySQL.
-
Overviewing Oozie
After watching this video; you will be able to recall the Oozie terminology.
-
Overviewing Pig
After watching this video; you will be able to describe Pig and its strengths.
-
Overviewing Sqoop
After watching this video; you will be able to describe Sqoop.
-
Performing a Sqoop Export
After watching this video; you will be able to perform a Sqoop export from HDFS into MySQL.
-
Performing a Sqoop Import
After watching this video; you will be able to perform a Sqoop import from MySQL into HDFS.
-
Pig Cogroup
After watching this video; you will be able to cogroup data with a Pig script.
-
Pig Command Line
After watching this video; you will be able to use the grunt shell with PigLatin.
-
Pig Data Types
After watching this video; you will be able to recall the complex data types used by Pig.
-
Pig Filtering
After watching this video; you will be able to use a Pig script to filter data.
-
Pig Flatten
After watching this video; you will be able to flatten data using a Pig script.
-
Pig ForEach
After watching this video; you will be able to use the ForEach operator with a Pig script.
-
Pig Functions
After watching this video; you will be able to write a Pig script to count data.
-
Pig Group
After watching this video; you will be able to group data using a Pig script.
-
Pig Join
After watching this video; you will be able to perform data joins using a Pig script.
-
Pig Operators
After watching this video; you will be able to recall some of the relational operators used by Pig.
-
Pig Parameters and Arguments
After watching this video; you will be able to set parameters and arguments in a Pig script.
-
Pig Scripts
After watching this video; you will be able to set parameters from both a text file and with the command line.
-
Pig Troubleshooting
After watching this video; you will be able to recall the different types of error categories.
-
Pig User-Defined Functions
After watching this video; you will be able to recall the languages that can be used to write user defined functions.
-
Preprocessing Data
After watching this video; you will be able to describe dirty data and how it should be preprocessed.
-
Running a Streaming Job
After watching this video; you will be able to stream a Python job.
-
Running an Oozie Workflow
After watching this video; you will be able to run an Oozie workflow job.
-
Selecting Additional Ecosystem Components
After watching this video; you will be able to recall some other popular components for the Hadoop Ecosystem.
-
Selecting an Environment
After watching this video; you will be able to recall the minimum system requirements for installation.
-
Setting Up Hadoop
After watching this video; you will be able to describe the three different installation modes.
-
Setting Up Hue
After watching this video; you will be able to recall the configuration files that must be edited.
-
Setting Up MySQL for Hive
After watching this video; you will be able to create a table in MySQL using Hive.
-
Setting Up Oozie
After watching this video; you will be able to recall the two categories of environmental variables for configuring Oozie.
-
Setting up SSH
After watching this video; you will be able to setup SSH for Hadoop.
-
Sqoop and Hbase
After watching this video; you will be able to recall that you must execute a Sqoop import statement for each data element.
-
Sqoop and Hive
After watching this video; you will be able to recall the parameters that must be set in the Sqoop import statement.
-
Sqoop and Hive Exports
After watching this video; you will be able to recall the parameters that must be set in the Sqoop export statement.
-
Sqoop Troubleshooting
After watching this video; you will be able to recall how to use chain troubleshooting to resolve Sqoop issues.
-
Starting and Stopping HDFS
After watching this video; you will be able to start and stop Hadoop HDFS.
-
Starting and Stopping YARN
After watching this video; you will be able to start and stop Hadoop YARN.
-
Stress Testing Hadoop
After watching this video; you will be able to validate the installation and configuration.
-
Submitting an Oozie Workflow
After watching this video; you will be able to submit an Oozie workflow job.
-
Terms for Data
After watching this video; you will be able to describe the two different types of data.
-
The Driver Class
After watching this video; you will be able to describe the function of the MapReduceDriver Java class.
-
The History of Hadoop Versions
After watching this video; you will be able to recall why version 2.0 was significant.
-
The Mapper Class
After watching this video; you will be able to describe the base mapper class of the MapReduce Java API and describe how to override its methods.
-
The Principle of Embracing Failure
After watching this video; you will be able to describe the design principles of embracing failure.
-
The Principle of Sharing Nothing
After watching this video; you will be able to describe the design principles of sharing nothing.
-
The Purpose of HCatalog
After watching this video; you will be able to describe Hcatalog.
-
The purpose of metastore and HiveServer2
After watching this video; you will be able to describe metastore and hiveserver2.
-
The Reducer Class
After watching this video; you will be able to describe the base Reducer class of the MapReduce Java API and describe how to override its methods.
-
The World of Data
After watching this video; you will be able to describe the data life cycle management.
-
Timestamping with Flume
After watching this video; you will be able to create a Flume agent with a TimeStampInterceptor.
-
Troubleshooting Hadoop
After watching this video; you will be able to recall some of the most common errors and how to fix them.
-
Troubleshooting HDFS
After watching this video; you will be able to troubleshoot HDFS errors.
-
Troubleshooting Installation Errors
After watching this video; you will be able to access Hadoop logs and troubleshoot Hadoop installation errors.
-
Using a Hive Explain Plan
After watching this video; you will be able to use a Hive explain plan.
-
Using Avro Source
After watching this video; you will be able to use Avro to capture a remote file.
-
Using cURL for Web Data
After watching this video; you will be able to use cURL to download web server data.
-
Using Derby for Hive
After watching this video; you will be able to create a table in Derby using Hive.
-
Using HCatalog
After watching this video; you will be able to use HCatalog.
-
Using the HBase Command Line
After watching this video; you will be able to use the HBase command line to create tables and insert data.
-
WordCount; the Hello World of Hadoop
After watching this video; you will be able to load a large text book and then run WordCount to count the number of words in the text book.
-
Working with Date data types
After watching this video; you will be able to recall the three most common date datatypes and which systems support each.
-
Working with HBase Data
After watching this video; you will be able to create and change HBase data.
-
Working with HBase Tables
After watching this video; you will be able to manage tables and view the web interface.
-
Writing a First Hive Script
After watching this video; you will be able to write and use Hive scripts.
-
Writing a Hive Partition Script
After watching this video; you will be able to write a Hive partition script.
-
Writing a MapReduce Job
After watching this video; you will be able to build a JAR file and run WordCount.
-
Writing a MapReduce Job for Inventory
After watching this video; you will be able to set up the classpath and test a MapReduce job.
-
YARN ApplicationMaster
After watching this video; you will be able to diagram YARN Application Master and identify its key components.
-
YARN Daemons on the Data Server
After watching this video; you will be able to describe the YARN NodeManager and ApplicationMaster daemons.
-
YARN Daemons on the Master Server
After watching this video; you will be able to describe the roles of the Resource Manager daemon.
-
YARN Job Failure
After watching this video; you will be able to describe the operations of YARN.
-
YARN Key Concepts
After watching this video; you will be able to list the components of YARN and identify their primary functions.
-
YARN Node Manager
After watching this video; you will be able to diagram YARN Node Manager and identify its key components.
-
YARN Resource Manager
After watching this video; you will be able to diagram YARN Resource Manager and identify its key components.
-
Adding DataNodes
After watching this video; you will be able to add a DataNode to a Hadoop cluster.
-
Administering HDFS
After watching this video; you will be able to describe the tools fsck and dfsadmin.
-
Balancing Hadoop Clusters
After watching this video; you will be able to balance a Hadoop cluster.
-
Balancing Resources
After watching this video; you will be able to describe how resources are distributed over the total capacity.
-
Building Client Servers
After watching this video; you will be able to build images for required servers in the Hadoop cluster.
-
Building Hadoop Clients
After watching this video; you will be able to build the Hadoop clients.
-
Building Images for Baseline Servers
After watching this video; you will be able to build an image for a baseline server.
-
Building Images for DataServers
After watching this video; you will be able to build an image for a DataServer.
-
Building Images for Master Servers
After watching this video; you will be able to build an image for a Master Server.
-
Calculating Storage Amounts
After watching this video; you will be able to calculate the correct number of disks required for a storage solution.
-
Configure Logging for Jobs
After watching this video; you will be able to describe how to configure Hadoop jobs for logging.
-
Configure Speculative Execution
After watching this video; you will be able to configure speculative execution.
-
Configuring for High Availability
After watching this video; you will be able to edit the Hadoop configuration files for high availability.
-
Configuring Hadoop Clusters
After watching this video; you will be able to configure a Hadoop cluster.
-
Configuring Hadoop for AWS
After watching this video; you will be able to prepare to install and configure a Hadoop cluster on AWS.
-
Configuring Hadoop Logs
After watching this video; you will be able to configure Hadoop logs.
-
Configuring Hcatalog Daemons
After watching this video; you will be able to configure Hcatalog daemons.
-
Configuring HDFS for Kerberos
After watching this video; you will be able to configure HDFS for Kerberos.
-
Configuring Hive Daemons
After watching this video; you will be able to configure Hive daemons.
-
Configuring Hive for Kerberos
After watching this video; you will be able to configure Hive for Kerberos.
-
Configuring Hue for Kerberos
After watching this video; you will be able to configure Hue for use with Kerberos.
-
Configuring JobHistoryServer Logs
After watching this video; you will be able to describe how to configure JogHistoryServer logs.
-
Configuring log4j for Hadoop
After watching this video; you will be able to describe how to configure log4j for Hadoop.
-
Configuring Logging
After watching this video; you will be able to configure logging for the Hadoop cluster.
-
Configuring Minimum Resources
After watching this video; you will be able to configure minimum share on the fair scheduler.
-
Configuring MySQL Databases
After watching this video; you will be able to configure a MySQL database.
-
Configuring Oozie for Kerberos
After watching this video; you will be able to configure Oozie for use with Kerberos.
-
Configuring Pig and HTTPFS for Kerberos
After watching this video; you will be able to configure Pig and HTTPFS for use with Kerberos.
-
Configuring Preemption
After watching this video; you will be able to configure preemption for the fair scheduler.
-
Configuring Single Resource Fairness
After watching this video; you will be able to configure single resource fairness.
-
Configuring YARN for Kerberos
After watching this video; you will be able to configure YARN for Kerberos.
-
Copying Data
After watching this video; you will be able to use distcp to copy data from one cluster to another.
-
Creating Access Control Lists
After watching this video; you will be able to create access control lists.
-
Creating an Amazon Cluster
After watching this video; you will be able to create an Amazon cluster.
-
Creating an Amazon Machine Image
After watching this video; you will be able to create an Amazon Machine Image.
-
Creating an AWS Account
After watching this video; you will be able to create an AWS account.
-
Creating an EC2 Baseline Server
After watching this video; you will be able to create an EC2 baseline server.
-
Creating High Availability Auto Failovers
After watching this video; you will be able to create an automated failover for the NameNode.
-
Creating Kerberos Diagrams
After watching this video; you will be able to diagram Kerberos and label the primary components.
-
Defining Cluster Management
After watching this video; you will be able to describe what cluster management entails and recall some of the tools that can be used.
-
Defining Hadoop Fault Tolerance
After watching this video; you will be able to describe how Hadoop leverages fault tolerance.
-
Defining HDFS High Availability
After watching this video; you will be able to describe the functions of Hadoop high availability.
-
Defining Supercomputing
After watching this video; you will be able to describe the principles of supercomputing.
-
Deploying Clusters
After watching this video; you will be able to use Cloudera Manager to deploy a cluster.
-
Deploying Hadoop Releases
After watching this video; you will be able to deploy a Hadoop release.
-
Deploying Support Tools
After watching this video; you will be able to distribute configuration files and admin scripts.
-
Design Considerations for Hadoop Clusters
After watching this video; you will be able to identify the hardware and networking recommendations for a Hadoop cluster.
-
Diagnosing with Cloudera Manager
After watching this video; you will be able to manage logs through Cloudera Manager.
-
Editing Oozie Workflows with Hue
After watching this video; you will be able to use Hue to edit Oozie workflows and coordinators.
-
Encrypting Data at Rest
After watching this video; you will be able to describe how to encrypt data at rest.
-
Encrypting Data in Motion
After watching this video; you will be able to encrypt data in motion.
-
Evaluating Storage Options
After watching this video; you will be able to compare the use of commodity hardware with enterprise disks.
-
Examining Additional Design Principles
After watching this video; you will be able to describe the design principles for move processing not data; embrace failure; and build applications not infrastructure.
-
Examining Amazon Web Services
After watching this video; you will be able to recall some of the most come services of the EC2 service bundle.
-
Examining Applications Modelling
After watching this video; you will be able to describe the purpose of application modelling.
-
Examining AWS Access Keys
After watching this video; you will be able to describe the use of AWS access keys.
-
Examining AWS Credentials
After watching this video; you will be able to describe how the AWS credentials are used for authentication.
-
Examining AWS Elastic MapReduce
After watching this video; you will be able to recall the advantages and limitations of using AWS EMR.
-
Examining Axioms of Supercomputing
After watching this video; you will be able to describe the three axioms of supercomputing.
-
Examining Benchmarking
After watching this video; you will be able to describe the practice of benchmarking on a Hadoop cluster.
-
Examining Best Practices for Benchmarking
After watching this video; you will be able to describe the different tools used for benchmarking a cluster.
-
Examining Best Practices for Network Tuning
After watching this video; you will be able to list some of the best practices for network tuning.
-
Examining Best Practices for Performance Tuning
After watching this video; you will be able to describe different strategies of performance tuning.
-
Examining Capacity Management
After watching this video; you will be able to compare the differences of availability versus performance.
-
Examining Capacity Strategies
After watching this video; you will be able to describe different strategies of resource capacity management.
-
Examining Cloud Computing
After watching this video; you will be able to describe how cloud computing can be used as a solution for Hadoop.
-
Examining Cloudera Manager
After watching this video; you will be able to describe the purpose and functionality of Cloudera Manager.
-
Examining Cluster Management Tools
After watching this video; you will be able to describe different tools from a functional perspective.
-
Examining Common Problems
After watching this video; you will be able to describe the categories of errors for a Hadoop cluster.
-
Examining Configuration Management Tools
After watching this video; you will be able to describe the configurations management tools.
-
Examining Data in Motion
After watching this video; you will be able to describe how to encrypt data in motion for Hadoop; Sqoop; and Flume.
-
Examining Data Server Recommendations
After watching this video; you will be able to recall some of the recommendations for a data server.
-
Examining DataNode Recovery
After watching this video; you will be able to describe the operation of the DataNode during a recovery.
-
Examining DataNode Reliability
After watching this video; you will be able to recall the most common causes for DataNode failure.
-
Examining Dominant Resource Fairness
After watching this video; you will be able to describe dominant resource fairness.
-
Examining EMR and End-users
After watching this video; you will be able to describe EMR End-user connections and EMR security levels.
-
Examining Engineering Teams
After watching this video; you will be able to recall the roles and skills needed for the Hadoop engineering team.
-
Examining Fair Scheduler Algorithms
After watching this video; you will be able to describe the primary algorithm and the configuration files for the fair scheduler.
-
Examining Fair Schedulers
After watching this video; you will be able to describe how the fair scheduling method allows all applications to get equal amounts of resource time.
-
Examining Flume and Kerberos
After watching this video; you will be able to describe how to configure Flume for use with Kerberos.
-
Examining Ganglia
After watching this video; you will be able to recall what Ganglia is and what it can be used for.
-
Examining Ganglia Functionality
After watching this video; you will be able to recall how Ganglia monitors Hadoop clusters.
-
Examining Hadoop Change Management
After watching this video; you will be able to describe the purpose of change management.
-
Examining Hadoop Cloud Implementations
After watching this video; you will be able to recall the advantages and limitations of using Hadoop in the cloud.
-
Examining Hadoop Cluster Architecture
After watching this video; you will be able to describe the different rack architectures for Hadoop.
-
Examining Hadoop logs
After watching this video; you will be able to describe how to manage logging levels.
-
Examining Hadoop Metrics2
After watching this video; you will be able to describe Hadoop Metrics2.
-
Examining Hadoop Security
After watching this video; you will be able to recall the primary security threats faced by the Hadoop cluster.
-
Examining Hardware Responsibilities
After watching this video; you will be able to recall the primary responsibilities for the master; data; and edge servers.
-
Examining HDFS Data Blocks
After watching this video; you will be able to describe the sizing and balancing of the HDFS data blocks.
-
Examining High Availability Auto Failovers
After watching this video; you will be able to recall the requirements for enabling an automated failover for the NameNode.
-
Examining Hive with Kerberos
After watching this video; you will be able to describe how to configure Hive for use with Kerberos.
-
Examining Hostnames and DNS Recommendations
After watching this video; you will be able to recall some of the recommendations for hostnames and DNS entries.
-
Examining Identification and Access Management
After watching this video; you will be able to describe AWS identification and access management.
-
Examining Input and Output Tune Up Options
After watching this video; you will be able to recall some of the rules for tuning the data node.
-
Examining Java Tune Up Options
After watching this video; you will be able to describe the purpose of Java tuning.
-
Examining Kerberos
After watching this video; you will be able to describe Kerberos and recall some of the common commands.
-
Examining MapReduce Job Management
After watching this video; you will be able to describe MapReduce job management on a Hadoop cluster.
-
Examining MapReduce Tune Up Options
After watching this video; you will be able to describe the configuration files and parameters used in performance tuning of MapReduce.
-
Examining Minimum Resources
After watching this video; you will be able to describe the minimum share function of the fair scheduler.
-
Examining MRv2
After watching this video; you will be able to describe the two major functions of the JobTracker.
-
Examining Nagios
After watching this video; you will be able to recall what Nagios is and what it can be used for.
-
Examining NameNode Recovery
After watching this video; you will be able to describe the operation of the NameNode during a recovery.
-
Examining NameNode Reliability
After watching this video; you will be able to recall the most common causes for NameNode failure.
-
Examining Network Clusters
After watching this video; you will be able to recall the best practices for different types of network clusters.
-
Examining Operating System Tune Up Options
After watching this video; you will be able to describe the configuration files and parameters used in performance tuning of the operating system.
-
Examining Pig; Sqoop; Oozie with Kerberos
After watching this video; you will be able to describe how to configure Pig; Sqoop; and Oozie for use with Kerberos.
-
Examining Preemption
After watching this video; you will be able to describe the preemption functions of the fair scheduler.
-
Examining Problem Management Best Practices
After watching this video; you will be able to recall some of the best practices for problem management.
-
Examining Rack Awareness
After watching this video; you will be able to describe rack awareness.
-
Examining Scheduler Behaviors
After watching this video; you will be able to describe the default behavior of the fair scheduler methods.
-
Examining Schedulers
After watching this video; you will be able to describe how schedulers perform various resource management.
-
Examining Security Risks
After watching this video; you will be able to describe the four pillars of the Hadoop security model.
-
Examining Single Resource Fairness
After watching this video; you will be able to describe the policy for single resource fairness.
-
Examining Single Resource Fairness Configurations
After watching this video; you will be able to identify different configuration options for single resource fairness.
-
Examining Storage Options
After watching this video; you will be able to describe the advantages of using a JBODs configuration.
-
Examining Tune Up Options for HDFS
After watching this video; you will be able to describe the configuration files and parameters used in performance tuning of HDFS.
-
Examining Tune UP Options for YARN
After watching this video; you will be able to describe the configuration files and parameters used in performance tuning of YARN.
-
Examining YARN Containers
After watching this video; you will be able to describe the functions of YARN containers.
-
Examining YARN High Availability
After watching this video; you will be able to describe the system view of the ResourceManager configurations set for high availability.
-
Examining YARN Job Reliability
After watching this video; you will be able to recall the most common causes of YARN job failure.
-
Examining YARN Task Reliability
After watching this video; you will be able to recall the most common causes for YARN task failure.
-
Exploring Amazon Web Services
After watching this video; you will be able to recall some of the most common services that Amazon offers.
-
Exploring Big Data Solutions
After watching this video; you will be able to recall the advantages and shortcomings of using Hadoop as a supercomputing platform.
-
Exploring Checkpoint Node
After watching this video; you will be able to recall the uses for the Checkpoint node.
-
Exploring Cloudera Manager Admin Console
After watching this video; you will be able to describe the different parts of the Cloudera Manager Admin Console.
-
Exploring Cloudera Manager Architecture
After watching this video; you will be able to describe the Cloudera Manager internal architecture.
-
Exploring Cluster Architecture
After watching this video; you will be able to describe the layout and structure of the Hadoop cluster.
-
Exploring DataNode Replications
After watching this video; you will be able to set up the DataNode for replication.
-
Exploring Design Principles for Hadoop
After watching this video; you will be able to describe the dumb hardware and smart software; and the share nothing design principles.
-
Exploring Event Management
After watching this video; you will be able to describe the importance of event management.
-
Exploring Ganglia
After watching this video; you will be able to describe how to use Ganglia to monitor a Hadoop cluster.
-
Exploring Incident Management
After watching this video; you will be able to describe the importance of incident management.
-
Exploring Master Server Best Practices
After watching this video; you will be able to recall some of the recommendations for a master server and edge server.
-
Exploring Operating Systems Best Practices
After watching this video; you will be able to recall some of the recommendations for an operating System.
-
Exploring Problem Management
After watching this video; you will be able to describe the different methodologies used for root cause analysis.
-
Exploring Problem Management Lifecycle
After watching this video; you will be able to describe the problem management lifecycle.
-
Exploring SSH Keys
After watching this video; you will be able to describe the use of SSH key pairs for remote access.
-
Exploring the AWS Command Line Interface
After watching this video; you will be able to describe what the command line interface is used for.
-
Format HDFS and Run a Hadoop Program
After watching this video; you will be able to install Hadoop on to the admin server.
-
Implementing Security Groups
After watching this video; you will be able to install security groups for AWS.
-
Importing Data with Hue
After watching this video; you will be able to import data using Hue.
-
Improving Performance
After watching this video; you will be able to improve cluster performance with Cloudera Manager.
-
Install Trash and Add a DataNode
After watching this video; you will be able to install Trash and add a DataNode.
-
Installing and Configuring Impala
After watching this video; you will be able to install and configure Impala.
-
Installing and Configuring Sentry
After watching this video; you will be able to install and configure Sentry.
-
Installing Cloudera Manager
After watching this video; you will be able to install Cloudera Manager.
-
Installing Compression
After watching this video; you will be able to install compression.
-
Installing Ganglia
After watching this video; you will be able to install Ganglia.
-
Installing Hadoop Metrics2 for Ganglia
After watching this video; you will be able to install Hadoop Metrics2 for Ganglia.
-
Installing Kerberos
After watching this video; you will be able to install Kerberos.
-
Installing Nagios
After watching this video; you will be able to install Nagios.
-
Installing Rack Awareness
After watching this video; you will be able to write configuration files for rack awareness.
-
Installing Trash
After watching this video; you will be able to install and configure trash.
-
Locking Down Networks
After watching this video; you will be able to recall the ports required for Hadoop and how network gateways are used.
-
Managing Hadoop Balancing
After watching this video; you will be able to describe the process for balancing a Hadoop cluster.
-
Managing Hadoop Service Levels
After watching this video; you will be able to monitor and improve service levels.
-
Managing Hadoop Upgrades
After watching this video; you will be able to plan an upgrade of a Hadoop cluster.
-
Managing HDFS
After watching this video; you will be able to use fsck and dfsadmin to check the HDFS file system.
-
Managing HDFS Backup and Recovery
After watching this video; you will be able to describe the operations involved for backing up data.
-
Managing HDFS DataNodes
After watching this video; you will be able to manage an HDFS DataNode.
-
Managing HDFS Scaling
After watching this video; you will be able to describe the operations for scaling a Hadoop cluster.
-
Managing Hosts with Cloudera Manager
After watching this video; you will be able to manage hosts with Cloudera Manager.
-
Managing Performance Tuning
After watching this video; you will be able to recall the two laws of performance tuning.
-
Managing Resources
After watching this video; you will be able to user Cloudera Manager to manage resources.
-
Managing User Access
After watching this video; you will be able to describe the use of POSIX and ACL for managing user access.
-
Managing User Security
After watching this video; you will be able to describe the security model for users on a Hadoop cluster.
-
Monitoring Fair Share
After watching this video; you will be able to monitor the behavior of Fair Share.
-
Monitoring Hadoop Security
After watching this video; you will be able to describe how to monitor Hadoop security.
-
Monitoring with Cloudera Manager
After watching this video; you will be able to use Cloudera Manager's monitoring features.
-
Moving Data Into AWS
After watching this video; you will be able to describe the various ways to move data into AWS.
-
Optimizing Memory for Containers
After watching this video; you will be able to recall why the Node Manager kills containers.
-
Optimizing Memory for Daemons
After watching this video; you will be able to describe the configuration files and parameters used in performance tuning of memory for daemons.
-
Optimizing Memory for YARN
After watching this video; you will be able to describe the purpose of memory tuning for YARN.
-
Perform Security Level task for Hadoop
After watching this video; you will be able to configure Hbase for Kerberos.
-
Performance Tuning HDFS
After watching this video; you will be able to performance tune HDFS.
-
Performance Tuning MapReduce
After watching this video; you will be able to tune up MapReduce for performance reasons.
-
Performing Cluster Management
After watching this video; you will be able to use Cloudera Manager to manage a cluster.
-
Performing MapReduce Job Management
After watching this video; you will be able to perform MapReduce job management on a Hadoop cluster.
-
Performing Root Cause Analysis
After watching this video; you will be able to conduct a root cause analysis on a major problem.
-
Planning a Deployment
After watching this video; you will be able to plan for the development of a Hadoop cluster.
-
Preparing for Kerberos Installation
After watching this video; you will be able to prepare for a Kerberos installation.
-
Provision Admin Servers
After watching this video; you will be able to provision an admin server.
-
Provisioning a Micro EC2
After watching this video; you will be able to provision a micro instance of EC2.
-
Provisioning Hadoop Clusters
After watching this video; you will be able to provision a Hadoop cluster.
-
Recover from a NameNode Failure
After watching this video; you will be able to Recover from a NameNode Failure.
-
Recovering Missing Data Blocks
After watching this video; you will be able to identify and recover from a missing data block scenario.
-
Replacing a DataNode
After watching this video; you will be able to use include and exclude files to replace a DataNode.
-
Run a Hadoop Program
After watching this video; you will be able to format HDFS; create an HDFS directory; import data; run a WordCount and view the results.
-
Running EMR Jobs
After watching this video; you will be able to run an EMR job from the Web Console.
-
Running EMR Jobs with Hue
After watching this video; you will be able to run an EMR job with Hue.
-
Running EMR Jobs with the Command Line Interface
After watching this video; you will be able to run an EMR job with the command line interface.
-
Running Hive Jobs with Hue
After watching this video; you will be able to run various Hive jobs using Hue.
-
Scaling Hadoop Architectures
After watching this video; you will be able to describe the best practices for scaling a Hadoop cluster.
-
Setting Cloudera Manger for High Availability
After watching this video; you will be able to set up Cloudera Manager for high availability.
-
Setting HDFS Quotas
After watching this video; you will be able to set quotas for the HDFS file system.
-
Setting Quotas
After watching this video; you will be able to set quotas for the HDFS file system.
-
Setting Up Checkpoint Servers
After watching this video; you will be able to provision a checkpoint server.
-
Setting Up EMR Clusters
After watching this video; you will be able to set up an EMR cluster.
-
Setting Up Flash Drive Installer
After watching this video; you will be able to set up a flash drive as boot media.
-
Setting Up Flash Drives
After watching this video; you will be able to setup a flash drive as book media.
-
Setting Up High Availability for ResourceManagers
After watching this video; you will be able to set up high availability for the ResourceManager.
-
Setting Up Identification and Access Management
After watching this video; you will be able to set up AWS IAM.
-
Setting Up NameNode High Availability
After watching this video; you will be able to set up a high availability solution for NameNode.
-
Setting Up Network Installer
After watching this video; you will be able to set up a network installer.
-
Setting Up S3
After watching this video; you will be able to set up S3 and import data.
-
Simulating Configuration Management Tools
After watching this video; you will be able to simulate a configuration management tool.
-
Starting and Stopping a Hadoop Cluster
After watching this video; you will be able to start and stop a Hadoop cluster.
-
Stress Testing and Benchmarking Hadoop Clusters
After watching this video; you will be able to perform a benchmark of a Hadoop cluster.
-
Swapping NameNodes
After watching this video; you will be able to swap to a new NameNode.
-
Testing Application Reliability
After watching this video; you will be able to test application reliability.
-
Testing Data Blocks
After watching this video; you will be able to describe the use of TestDFSIO.
-
Testing DataNode Reliability
After watching this video; you will be able to test the availability for the DataNode.
-
Testing NameNode Failure
After watching this video; you will be able to test the availability for the NameNode.
-
Testing YARN Container Reliability
After watching this video; you will be able to test YARN container reliability.
-
Tuning a Hadoop Cluster
After watching this video; you will be able to optimize memory and benchmark a Hadoop cluster.
-
Tuning Memory for Hadoop Clusters
After watching this video; you will be able to performance tune memory for the Hadoop cluster.
-
Use Monitoring Tools
After watching this video; you will be able to use different monitoring tools to identify problems; failures; errors and solutions.
-
Using a Hadoop Cluster on AWS
After watching this video; you will be able to write an Elastic MapReduce script for AWS.
-
Using Cloudera Manager for Administration
After watching this video; you will be able to perform backups; snapshots; and upgrades using Cloudera Manager.
-
Using Ganglia
After watching this video; you will be able to use Ganglia to monitor a Hadoop cluster.
-
Using Hadoop Metrics2 for Nagios
After watching this video; you will be able to use Hadoop Metrics2 for Nagios.
-
Using Hive for Sentry Administration
After watching this video; you will be able to implement security administration using Hive.
-
Using Nagios
After watching this video; you will be able to use Nagios to monitor a Hadoop cluster.
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Using Nagios Commands
After watching this video; you will be able to use Nagios commands.
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Using the AWS Command Line Interface
After watching this video; you will be able to use the command line interface.
-
Using the Fair Scheduler
After watching this video; you will be able to use the fail scheduler with multiple users.
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Validating Flume; Sqoop; HDFS; and MapReduce
After watching this video; you will be able to test the functionality of Flume; Sqoop; HDFS; and MapReduce.
-
Validating Hive and Pig
After watching this video; you will be able to test the functionality of Hive and Pig.
-
Writing Init Scripts
After watching this video; you will be able to write init scripts for Hadoop.
-
Writing Service Levels
After watching this video; you will be able to write service levels for performance.
-
Accessing the CLI
After watching this video; you will be able to access the Splunk CLI.
-
Add Dashboard Inputs
After watching this video; you will be able to add an input to a dashboard.
-
Adding a New Dashboard
After watching this video; you will be able to create a Splunk dashboard.
-
Adding and Monitoring Files and Folders
After watching this video; you will be able to import data from files and monitor local files and folders.
-
Adding Sample Data to Splunk
After watching this video; you will be able to add the tutorial sample data to your Splunk install.
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After Performing a Splunk Search
After watching this video; you will be able to navigate the results of your Splunk search.
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Archiving Data
After watching this video; you will be able to archive indexed Splunk data for storage.
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Bar and Line Charts
After watching this video; you will be able to visualize data as bar and line charts in Splunk.
-
Before Running a Splunk Search
After watching this video; you will be able to prepare to run a search in Splunk.
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Change Default Values and License
After watching this video; you will be able to change the default values in a Splunk installation and apply a License.
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Chart Controls
After watching this video; you will be able to configure control features on visualizations.
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CLI Help Interface
After watching this video; you will be able to utilize the built-in help functions of the Splunk CLI.
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Configuration Files; Directories; and Structure
After watching this video; you will be able to work with Splunk configuration files.
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Configure a Search Head
After watching this video; you will be able to configure a Splunk node as a search head.
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Configure Forwarders to Use the Indexer Cluster
After watching this video; you will be able to configure Splunk to use forwarders with the Indexer Cluster.
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Configure Multiple Splunk Nodes
After watching this video; you will be able to manage multiple nodes in Splunk.
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Configuring Data Cloning
After watching this video; you will be able to implement data cloning on a Splunk forwarder.
-
Configuring Dynamic Drilldown Using Simple XML
After watching this video; you will be able to configure dynamic drilldown in charts using simple XML.
-
Configuring Load Balancing
After watching this video; you will be able to implement load balancing on a Splunk forwarder.
-
Configuring Splunk Inputs
After watching this video; you will be able to configure data sources for Splunk.
-
Configuring Splunk Networking
After watching this video; you will be able to bind Splunk to an IPv4 and/or IPv6 address.
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Configuring Timestamps
After watching this video; you will be able to configure how Splunk stores the date and time with your data.
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Considerations for Index Backups
After watching this video; you will be able to identify Splunk Backup Requirements.
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Contextual Drilldown
After watching this video; you will be able to specify context driven drilldown behavior.
-
Create a Bar Chart
After watching this video; you will be able to create charts from Splunk data.
-
Create a Dashboard from a Search and the Edit Panels
After watching this video; you will be able to save a search as a dashboard and edit panels in dashboards.
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Create a Splunk Role and User
After watching this video; you will be able to create users and roles in Splunk.
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Create Dashboard PDFs
After watching this video; you will be able to generate PDF reports from Splunk Dashboards.
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Creating a New Source Type for Event Processing
After watching this video; you will be able to improve Splunk's processing of events.
-
Creating a Pivot
After watching this video; you will be able to create and save a Splunk Pivot.
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Creating a Pivot Chart
After watching this video; you will be able to create a Splunk Pivot chart.
-
Creating Base Searches for Dashboards
After watching this video; you will be able to save base searches from which to build dashboard searches.
-
Creating Child Objects
After watching this video; you will be able to define child objects under Splunk parent objects.
-
Creating New Data Models
After watching this video; you will be able to create a new data model in Splunk.
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Creating Per-Result and Rolling-Window Alerts
After watching this video; you will be able to construct per-result and rolling-window alerts in Splunk.
-
Creating Scheduled Alerts
After watching this video; you will be able to create scheduled alerts in Splunk.
-
Creating Triggered Alerts
After watching this video; you will be able to construct triggered alerts.
-
Creating Users and Roles
After watching this video; you will be able to add and manage users and roles in Splunk.
-
Dashboard Searches
After watching this video; you will be able to implement searches in simple XML dashboards.
-
Data Forwarder Overview
After watching this video; you will be able to compare Splunk data forwarders.
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Data Retirement and Aging
After watching this video; you will be able to configure how Splunk handles aged data.
-
Default Dashboards and Customization
After watching this video; you will be able to identify default Splunk dashboards and customize the banner messages.
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Defining a Root Object
After watching this video; you will be able to edit and add model objects and root objects in Splunk.
-
Defining Event Types
After watching this video; you will be able to define types of events for data classification.
-
Deleting Indexed Data and Indexes
After watching this video; you will be able to remove data that has been indexed or indexes entirely.
-
Deploy a Heavy Forwarder
After watching this video; you will be able to implement a heavy Splunk forwarder.
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Deploy the Unix Universal Forwarder
After watching this video; you will be able to install the universal forwarder in Unix environments.
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Deploy the Windows Universal Forwarder
After watching this video; you will be able to install the universal fowarder in Windows.
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Deploying Multiple Indexes
After watching this video; you will be able to configure multiple indexes in Splunk.
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Drilldown Introduction
After watching this video; you will be able to work with visualization drilldown.
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Drilldown on Additional Chart Types
After watching this video; you will be able to configure visualization on additional chart types.
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Dynamic Drilldown
After watching this video; you will be able to define dynamic drilldown behavior.
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Editing Dashboards
After watching this video; you will be able to modify existing dashboards in Splunk.
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Effect of Incoming Data on Performance
After watching this video; you will be able to identify data performance issues in Splunk.
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Effect of Users and Searches on Performance
After watching this video; you will be able to identify other performance issues in Splunk.
-
Enable Peer Nodes
After watching this video; you will be able to enable Splunk peer nodes.
-
Enable the Master Node
After watching this video; you will be able to enable the Splunk indexer cluster master node.
-
Executing Secondary Searches
After watching this video; you will be able to create a secondary search on Splunk results.
-
Extending Alert Functionality
After watching this video; you will be able to edit and expand the functionality of a Splunk alert.
-
Generating Tables
After watching this video; you will be able to create tables from a Splunk search.
-
Identifying Data Patterns
After watching this video; you will be able to use the Splunk patterns tab to identify data patterns.
-
Installing Splunk on a Windows Server
After watching this video; you will be able to install a Splunk instance.
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Installing Splunk on Linux
After watching this video; you will be able to install Splunk on several Linux platforms.
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Installing Splunk on Windows
After watching this video; you will be able to install Splunk on Windows systems.
-
Introducing Indexer Clusters
After watching this video; you will be able to describe the steps in deploying indexer clusters.
-
Introduction to Alerts
After watching this video; you will be able to identify the different types of alerts in Splunk.
-
Introduction to Dashboard Editor
After watching this video; you will be able to access the dashboard editor in Splunk.
-
Introduction to Editing Simple XML
After watching this video; you will be able to modify dashboards by editing simple XML.
-
Introduction to Splunk
After watching this video; you will be able to identify the functions and abilities of Splunk.
-
Investigate Events Using the Timeline
After watching this video; you will be able to utilize the timeline view to analyze Splunk events.
-
Locating and Sharing Reports
After watching this video; you will be able to find saved reports and share them.
-
Migrating to a New Server
After watching this video; you will be able to migrate indexes and data to a new server.
-
Monitoring Network Ports and Splunk Forwarders
After watching this video; you will be able to import data from network ports and Splunk forwarders.
-
Moving the Index Database
After watching this video; you will be able to change the location of the Splunk index database.
-
Navigating the Splunk Interface
After watching this video; you will be able to navigate the Splunk bar and Apps.
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Other Alert Actions
After watching this video; you will be able to configure alternate alert modes in Splunk.
-
Other Charting Types
After watching this video; you will be able to utilize other chart types in Splunk.
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Pie; Scatter; and Bubble Charts
After watching this video; you will be able to display data as pie; scatter; and bubble charts.
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Pivot Overview
After watching this video; you will be able to recognize the components of the Pivot interface.
-
Precedence of Splunk Configuration Files
After watching this video; you will be able to identify the order in which Splunk applies configuration directives and attributes.
-
Restarting Splunk Nodes
After watching this video; you will be able to restart a single Splunk node or an entire cluster.
-
Restoring Data
After watching this video; you will be able to restore previously archived Splunk data.
-
Retrieve Events Using Fields
After watching this video; you will be able to use data fields to find Splunk events.
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Retrieve Events with Search
After watching this video; you will be able to use the Splunk search command to find events.
-
Running Splunk Without Administrator Permissions
After watching this video; you will be able to use Splunk without Administrator access.
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Save a Pivot as a Dashboard Panel
After watching this video; you will be able to save pivots to the dashboard.
-
Saving and Viewing Reports
After watching this video; you will be able to save reports and view saved reports.
-
Scaling Your Deployment
After watching this video; you will be able to scale a Splunk deployment to meet requirements.
-
Search Actions and Modes
After watching this video; you will be able to control search job progress and mode in Splunk.
-
Searching for Data in Splunk
After watching this video; you will be able to use the Search features to find data.
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Sending an Alert E-mail
After watching this video; you will be able to configure Splunk to send an e-mail during an alert condition.
-
Setting Data Source Type
After watching this video; you will be able to change the type of data source used by Splunk to index your data.
-
Specifying Search Indexes and Permissions
After watching this video; you will be able to control the indexes used to perform a search and who has access to them.
-
Splunk Data Models and Objects
After watching this video; you will be able to describe data models and objects in Splunk.
-
Splunk Licensing
After watching this video; you will be able to identify the types of Splunk licenses and choose what best suits your requirements.
-
Splunk Web Introduction
After watching this video; you will be able to log in to and identify Splunk Web administration components.
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Start and Stop Splunk Enterprise
After watching this video; you will be able to start and stop the Splunk service on multiple platforms; and configure Splunk to start at boot.
-
Starting Splunk
After watching this video; you will be able to start the Splunk service on multiple operating systems.
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Storage Requirements
After watching this video; you will be able to determine Splunk storage requirements.
-
Take a Node Offline
After watching this video; you will be able to remove a Splunk peer from operation for maintenance.
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Throttling Alert Rates and Alert Permissions
After watching this video; you will be able to utilize throttling to limit Splunk alerts and set alert permissions.
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Tokens and Dashboards
After watching this video; you will be able to utilize Splunk tokens in Dashboards using simple XML.
-
Upgrading Splunk
After watching this video; you will be able to upgrade an existing Splunk instance on a Unix or Windows system.
-
Using Attributes from Lookup Tables
After watching this video; you will be able to add attributes from Lookup tables to your Splunk data model object.
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Using Automatically Extracted Attributes
After watching this video; you will be able to add attributes that were automatically extracted to your Splunk data model object.
-
Using Dashboards
After watching this video; you will be able to work with Splunk dashboards.
-
Using Search Macros
After watching this video; you will be able to create Splunk macros to simplify searches.
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Using the Splunk Data Summary
After watching this video; you will be able to view the data sources available to you for searching.
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Using the Time Range Picker
After watching this video; you will be able to specify a time range for the results of your search.
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View the Master Dashboard
After watching this video; you will be able to access the dashboard of the Splunk cluster master.
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View the Peer and Search Head Dashboards
After watching this video; you will be able to access the Splunk dashboard of the peers and the search head.
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Viewing Search Results
After watching this video; you will be able to navigate the search result tabs.
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Visualizing Events
After watching this video; you will be able to access visualizations of Splunk events.
-
Working with Splunk Configuration Files
After watching this video; you will be able to copy and edit Splunk configuration files.
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Data Science
Data science; driven by Big Data from every sphere; enables the continuing rapid creation of data products – evident in the accuracy of recent trend-spotting technologies. In this video; Charles Robinson explains the role of data scientists; proficient in a range of specific skills including visualization; in helping companies with inadequate infrastructures to transition their data to more structured states.
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In Practice
The organizational processing and analysis of Big Data requires large investments in human; hardware; and software resources; a flexible architecture accommodating global and local change; and scalable; cloud-based hosting solutions. In this video; Charles Robinson considers data-warehouse cleaning and the cost-savings that can be effected in infrastructure; staffing; rapid services deployment; and data transportation by choosing a cloud-based hosting solution.
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Statistics
The conversion of structured and unstructured Big Data into meaningful information by statisticians promises to enable decision automation. In this video; Charles Robinson uses published statistics to illustrate the demands being placed on the technology stacks making up the Big Data landscape; and outlines the costs of failing to differentiate between; good and bad information.
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Configuring Index Disk Usage
After watching this video; you will be able to configure the size and disk usage of Splunk indexes.
-
CouchDB Options
When managing NoSQL databases with CouchDB; the Futon we-based interface provides easy access to configuration options; diagnostic information; and support material. In this video; Chuck Easttom demonstrates the options that allow you to edit configurable parameters; configure replication; view status and installation information; and access documentation and support material in Futon.
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Document Based Data Model
The document-based data model is one of four major NoSQL data models. In this video; Chuck Easttom discusses how document stores contain more diverse; rich; and complicated data types than other data models for NoSQL implementations.
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Graph Based Data Model
The graph-based data model is inspired by the mathematical concept of graph theory and is one of the data models used in NoSQL data store implementations. In this video; Chuck Easttom demonstrates the components of graph-based data models.
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Architecture
Big Data architecture needs to eliminate conventional data-warehousing shortcomings while processing and refining to meaningful output vast volumes of inbound structured and unstructured data from multiple ambiguous sources. In this video; Charles Robinson details these needs and demands; and introduces the value of architectural governance from design to productivity through the Big Data Architecture Framework (BDAF).
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Creating a Pivot Table
After watching this video; you will be able to create and save a Pivot table.
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Configuring Oozie
After watching this video; you will be able to configure Oozie.
-
Creating a Database
After watching this video; you will be able to create a database into MySQL.
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Decision Trees
Decision trees; comprising decision; chance; and end nodes; are effective; high-speed algorithms used in conjunction with data parallelism to sort and split data; and compare results and possible consequences. In this video; Charles Robinson explains the benefits and drawbacks of decision trees; and details the processes by which they are quickly and accurately able to construct large datasets.
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Installing ZooKeeper
After watching this video; you will be able to install and configure ZooKeeper.
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MapReduce
To prevent large datasets overwhelming sequential algorithms used to analyze Big Data; MapReduce processes the challenges of large datasets on a cluster of computer nodes. In this video; Charles Robinson introduces MapReduce's map and reduce functions; whereby data problems are split and fed to subnodes for processing before being fed back to the master node for parsing and output.
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MapReduce
After watching this video; you will be able to label all of the functions for MapReduce on a diagram.
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Analytics
Big Data Analytics; or extracting information useful to company decision-making from large; often-unstructured datasets; is fast surpassing the capabilities of conventional data warehouses. In this video; Charles Robinson outlines the toolsets data scientists need; the challenges new technologies and platforms must overcome; and the investments companies must make to effectively manage and exploit larger information volumes and infrastructures.
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Architecture
After watching this video; you will be able to describe the Accumulo Architecture.
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Components
Big Data technology is made up of many open source software tools. Such as Flume; Hive; and Pig. In the video; Will Dailey discusses the main software components of Big Data.
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Establishing a Connection
After watching this video; you will be able to establish a connection to HBase using a Java API with the HConnection; HTable; and HTablePool.
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Updating Data
After watching this video; you will be able to update data in an HBase table using the Put instance.
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Defining Big Data
After watching this video; you will be able to describe what big data is and is not.
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Replication
After watching this video; you will be able to describe strategies for replication in Apache Cassandra.
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Introduction
Big Data exceeds conventional database-processing storage capacities. Companies therefore need more previously-hidden analytical and enabling information quickly and cost-effectively to retain competitive edge and profitability. In this video; Charles Robinson introduces the analytical and product-enabling features of Big Data and defines properties that allow its proper analysis - volume; velocity; and variety.
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Multi-sink Flume Agents
After watching this video; you will be able to create multi-sink Flume agents.
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Defining Big Data
After watching this video; you will be able to define Big Data.
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Managing Services
After watching this video; you will be able to manage Cloudera Manager's services.
-
Serialization
After watching this video; you will be able to configure serialization and deserialization in Apache Kafka.
-
Defining Big Data
After watching this video; you will be able to describe what big data is.
-
Components
After watching this video; you will be able to describe the HBase components and their functionalities.
-
Configuring Hue
After watching this video; you will be able to configure the hue.ini file.
-
Configuring Hue with MySQL
After watching this video; you will be able to configure Hue using MySQL.
-
Deleting Data
After watching this video; you will be able to delete data from an HBase table.
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Distributed Computing
In Big Data; for super-computing to work the load must be distributed. Hadoop software facilitates this distributed computing model and allows for a proper flow of data. In this video; Will Dailey demonstrates the requirements for building a Hadoop Cluster.
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Introducing
Big Data completely changes the way data flows throughout the globe and it affects everyone. In this video; Will Dailey introduces Big Data and the concept of Global Data Fabric.
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Installing Hadoop
After watching this video; you will be able to download and install Apache Hadoop.
-
Introduction
After watching this video; you will be able to describe Accumulo and its characteristics.
-
Monitoring Applications
After watching this video; you will be able to monitor applications.
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Designing and Implementing Big Data Analytics: Apache Ambari
After watching this video, you will be able to manage and monitor clusters with Apache Ambari.
-
Designing and Implementing Big Data Analytics: Oozie for Apache Hadoop
After watching this video, you will be able to manage workflows with Oozie.
-
Designing and Implementing Big Data Analytics: HCatalog
After watching this video, you will be able to use HCatalog for table and storage management in Hadoop.
-
Designing and Implementing Big Data Analytics: Apache Zookeeper
After watching this video, you will be able to recognize the key functions of Apache Zookeeper.
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Designing and Implementing Big Data Analytics: Apache Pig
After watching this video, you will be able to use Apache Pig for data analysis.
-
Designing and Implementing Big Data Analytics: Interactive Hive in HDInsight
After watching this video, you will be able to recognize the capabilities of Interactive Hive in HDInsight.
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Designing and Implementing Big Data Analytics: R on HDInsight
After watching this video, you will be able to identify how R is used with HDInsight.
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Designing and Implementing Big Data Analytics: Choosing the Right Tools
After watching this video, you will be able to determine which tools to use and identify important security features.
-
Designing and Implementing Big Data Analytics: Compute Cluster Features
After watching this video, you will be able to recognize key features and capabilities of various tools used with HDInsight.
-
Designing and Implementing Big Data Analytics: Apache Sqoop
After watching this video, you will be able to recognize key features of Apache Sqoop.
-
Designing and Implementing Big Data Analytics: Importing Data with Apache Sqoop
After watching this video, you will be able to demonstrate how to import data from an RDBMS to the Hadoop Distributed File System.
-
Designing and Implementing Big Data Analytics: Hadoop and HDInsight Clusters
After watching this video, you will be able to recognize features of Hadoop and HDInsight clusters.
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Designing and Implementing Big Data Analytics: Apache Spark on HDInsight
After watching this video, you will be able to identify how Apache Spark is used with HDInsight.
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Designing and Implementing Big Data Analytics: HBase in HDInsight
After watching this video, you will be able to recognize the capabilities of HBase in HDInsight.
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Designing and Implementing Big Data Analytics: Apache Kafka on HDInsight
After watching this video, you will be able to identify how Apache Kafka is used with HDInsight.
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Designing and Implementing Big Data Analytics: Apache Storm and Apache Flume
After watching this video, you will be able to recognize key features of Apache Storm and Apache Flume.
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Designing and Implementing Big Data Analytics: Azure Cosmos DB and DocumentDB
After watching this video, you will be able to recognize key features of Azure Cosmos DB and DocumentDB.
-
Designing and Implementing Big Data Analytics: ASP.NET Application Data
After watching this video, you will be able to store and access .NET web application data with Azure Cosmo DB.
-
Designing and Implementing Big Data Analytics: Microsoft Azure Storage Explorer
After watching this video, you will be able to install and use the Microsoft Azure Storage Explorer.
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Designing and Implementing Big Data Analytics: Azure SQL Data Warehouse
After watching this video, you will be able to load data into an Azure SQL Data Warehouse.
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Designing and Implementing Big Data Analytics: Data Query with PolyBase
After watching this video, you will be able to install and use PolyBase to query data in an Azure Storage account.
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Designing and Implementing Big Data Analytics: Moving Data to Azure Virtual Machine
After watching this video, you will be able to recognize common methods for moving data from an on-premises SQL Server to an Azure Virtual Machine SQL Server.
-
Designing and Implementing Big Data Analytics: Azure Data Factory and the Azure Data Lake Store
After watching this video, you will be able to list key features of the Azure Data Factory and the Azure Data Lake Store.
-
Designing and Implementing Big Data Analytics: Azure PowerShell
After watching this video, you will be able to use Azure PowerShell with Azure Storage.
-
Designing and Implementing Big Data Analytics: Data Considerations for Azure HDInsight
After watching this video, you will be able to recognize best practices and considerations for data collection and loading in HDInsight.
-
Designing and Implementing Big Data Analytics: Azure Big Data Solutions
After watching this video, you will be able to identify basic features of Microsoft big data solutions.
-
Designing and Implementing Big Data Analytics: Azure Storage Solutions
After watching this video, you will be able to recognize storage options for big data and identify methods to load data into Azure Blob storage.
-
Designing and Implementing Big Data Analytics: Lambda Speed Layer
After watching this video, you will be able to list considerations for the Lambda speed layer design.
-
Designing and Implementing Big Data Analytics: Lambda Architecture Capabilities and Limitations
After watching this video, you will be able to recognize the key capabilities and limitations of the Lambda architecture.
-
Designing and Implementing Big Data Analytics: The Kappa Architecture
After watching this video, you will be able to recognize the difference between Lambda and Kappa architectures.
-
Designing and Implementing Big Data Analytics: Traditional Analytics Approaches
After watching this video, you will be able to recognize traditional data analytics approaches and how they differ from streaming solutions.
-
Designing and Implementing Big Data Analytics: Real-time Data Analytics Value Proposition
After watching this video, you will be able to recognize how value is generated through real-time data analytics solutions.
-
Designing and Implementing Big Data Analytics: Azure Stream Analytics
After watching this video, you will be able to identify how Azure Stream Analytics work.
-
Designing and Implementing Big Data Analytics: Capabilities and Benefits of Azure Stream Analytics
After watching this video, you will be able to recognize the benefits and capabilities of Azure Stream analytics.
-
Designing and Implementing Big Data Analytics: Apache Storm and Azure Stream Analytics
After watching this video, you will be able to compare Apache Storm and Azure Stream Analytics.
-
Designing and Implementing Big Data Analytics: Lambda Serving Layer
After watching this video, you will be able to identify considerations for the Lambda serving layer design.
-
Designing and Implementing Big Data Analytics: Application of the Lambda Architecture
After watching this video, you will be able to recognize what the Lambda architecture is and how it is used.
-
Designing and Implementing Big Data Analytics: Lambda Batch Layer
After watching this video, you will be able to list considerations for the Lambda batch layer design.
-
Designing and Implementing Big Data Analytics: Types of Data Partitioning Schemes
After watching this video, you will be able to recognize various data partitioning schemes.
-
Designing and Implementing Big Data Analytics: Designing Effective Data Partitions
After watching this video, you will be able to design partitions for scalability, query performance, and availability.
-
Designing and Implementing Big Data Analytics: Enterprise Analytics Architecture
After watching this video, you will be able to describe fundamental architectural concepts of enterprise analytics.
-
Designing and Implementing Big Data Analytics: Real-time Analytics Processing Components
After watching this video, you will be able to recognize key components for real-time event processing.
-
Designing and Implementing Big Data Analytics: Create Clusters in HDInsight Using Azure PowerShell
After watching this video, you will be able to create Linux-based clusters in HDInsight using Azure PowerShell.
-
Designing and Implementing Big Data Analytics: Manage Clusters in HDInsight Using Azure PowerShell
After watching this video, you will be able to manage Hadoop clusters in HDInsight using Azure PowerShell.
-
Designing and Implementing Big Data Analytics: Multi-node Storm Cluster
After watching this video, you will be able to list main phases in setting up a storm cluster.
-
Designing and Implementing Big Data Analytics: Data Ingesting and Compute Resource Design
After watching this video, you will be able to use data ingesting tools for real-time analytics.
-
Designing and Implementing Big Data Analytics: Receive Events Using Apache Storm
After watching this video, you will be able to receive events from Event Hubs using Apache Storm.
-
Designing and Implementing Big Data Analytics: Enable Event Hubs Capture
After watching this video, you will be able to enable Event Hubs capture using the Azure portal.
-
Designing and Implementing Big Data Analytics: Row Key Design in HBase
After watching this video, you will be able to recognize aspects of row key design in HBase.
-
Designing and Implementing Big Data Analytics: Apache NiFi Architecture and Performance
After watching this video, you will be able to identify the Apache NiFi architecture and performance characteristics.
-
Designing and Implementing Big Data Analytics: Key Features of Apache NiFi
After watching this video, you will be able to list key features of Apache NiFi.
-
Designing and Implementing Big Data Analytics: Overview of Azure Event Hubs
After watching this video, you will be able to recognize how Azure Event Hubs capabilities are used for real-time big data analytics.
-
Designing and Implementing Big Data Analytics: Key Features of Azure Event Hubs
After watching this video, you will be able to list key features of Azure Event Hubs.
-
Designing and Implementing Big Data Analytics: Create an Event Hub
After watching this video, you will be able to create an Event Hub using the Azure portal.
-
Designing and Implementing Big Data Analytics: Send Messages to Azure Event Hubs
After watching this video, you will be able to send messages to Azure Event Hubs in .NET Standard.
-
Designing and Implementing Big Data Analytics: Receive Messages Using the Event Processor Host
After watching this video, you will be able to receive messages with the event processor host in .NET Standard.
-
Designing and Implementing Big Data Analytics: Shared Access Signatures
After watching this video, you will be able to recognize key functions of Shared Access Signatures.
-
Designing and Implementing Big Data Analytics: Features of Batch Processing Technologies
After watching this video, you will be able to recognize key features and capabilities of batch processing technologies.
-
Designing and Implementing Big Data Analytics: Common Uses for Real-time Analytics Processing
After watching this video, you will be able to identify some common use cases for real-time analytics.
-
Designing and Implementing Big Data Analytics: Apache NiFi Overview
After watching this video, you will be able to recognize the Apache NiFi capabilities for real-time big data analytics.
-
Designing and Implementing Big Data Analytics: Managing Big Data with PowerShell
After watching this video, you will be able to use PowerShell to handle big data.
-
Designing and Implementing Big Data Analytics: SQL Server Analysis Services
After watching this video, you will be able to use SQL Server Analysis Services.
-
Designing and Implementing Big Data Analytics: Data Factory and Batch Processing
After watching this video, you will be able to process large datasets with Data Factory and Batch.
-
Designing and Implementing Big Data Analytics: Azure Data Security Features
After watching this video, you will be able to recognize Azure's technical data security capabilities.
-
Designing and Implementing Big Data Analytics: Role-based Access and Row Security
After watching this video, you will be able to features of role-based and row-based security.
-
Designing and Implementing Big Data Analytics: Data Management Gateway Security Settings
After watching this video, you will be able to configure firewall and proxy server settings.
-
Designing and Implementing Big Data Analytics: Apache Hive
After watching this video, you will be able to manage large datasets with Apache Hive.
-
Designing and Implementing Big Data Analytics: Azure Batch
After watching this video, you will be able to recognize key features and functionalities of Azure Batch.
-
Designing and Implementing Big Data Analytics: Apache Mahout
After watching this video, you will be able to recognize key features and functionalities of Apache Mahout.
-
Designing and Implementing Big Data Analytics: Spark SQL
After watching this video, you will be able to identify key features and data sources of Spark SQL.
-
Designing and Implementing Big Data Analytics: Hadoop MapReduce
After watching this video, you will be able to use MapReduce for writing applications.
-
Designing and Implementing Big Data Analytics: Create Alerts
After watching this video, you will be able to create alerts to get notified of errors.
-
Designing and Implementing Big Data Analytics: Batch Pipeline Action
After watching this video, you will be able to rerun selected activities and pause or resume multiple pipelines.
-
Designing and Implementing Big Data Analytics: Resource Explorer Tab
After watching this video, you will be able to navigate the Resource Explorer tab.
-
Designing and Implementing Big Data Analytics: Data Factory Scheduling and Execution
After watching this video, you will be able to configure activity and dataset scheduling.
-
Designing and Implementing Big Data Analytics: Dataset Availability
After watching this video, you will be able to configure dataset availability.
-
Designing and Implementing Big Data Analytics: Dataset Policies
After watching this video, you will be able to configure dataset policies.
-
Designing and Implementing Big Data Analytics: Data Slice Processing Concepts
After watching this video, you will be able to recognize data slicing features and concepts for parallel processing and re-running failed data slices.
-
Designing and Implementing Big Data Analytics: Multiple Activities
After watching this video, you will be able to identify how to chain multiple activities.
-
Designing and Implementing Big Data Analytics: Complex Dataset Schedules
After watching this video, you will be able to model complex dataset schedules.
-
Designing and Implementing Big Data Analytics: Features of Azure Data Factory
After watching this video, you will be able to create and publish a Data Factory and monitor pipelines with Azure Portal.
-
Designing and Implementing Big Data Analytics: Monitoring and Management App
After watching this video, you will be able to launch and navigate the Monitoring and Management app.
-
Designing and Implementing Big Data Analytics: Monitoring Pipelines with the Azure Portal
After watching this video, you will be able to monitor pipelines with the Azure Portal.
-
Designing and Implementing Big Data Analytics: Activity Policies
After watching this video, you will be able to identify the key policies that affect the run-time behavior of an activity in Azure Data Factory.
-
Designing and Implementing Big Data Analytics: Create and Publish Pipelines
After watching this video, you will be able to create and publish pipelines.
-
Designing and Implementing Big Data Analytics: Azure Data Factory Functions and Variables
After watching this video, you will be able to list Azure Data Factory functions, variables, and naming rules.
-
Designing and Implementing Big Data Analytics: Preparing to Create a Data Factory
After watching this video, you will be able to recognize the main steps and prerequisites to create and publish a Data Factory with Visual Studio.
-
Designing and Implementing Big Data Analytics: Create a Data Factory with Visual Studio
After watching this video, you will be able to create and publish a Data Factory with Visual Studio.
-
Designing and Implementing Big Data Analytics: Data Factory Datasets
After watching this video, you will be able to recognize the capabilities of Data Factory Datasets.
-
Designing and Implementing Big Data Analytics: Dataset Definitions and Properties
After watching this video, you will be able to identify key features of Data Factory Datasets.
-
Designing and Implementing Big Data Analytics: Dataset Structure
After watching this video, you will be able to recognize the structure of Data Factory Datasets.
-
Designing and Implementing Big Data Analytics: Create Datasets with Visual Studio
After watching this video, you will be able to create a Data Factory Dataset with Visual Studio.
-
Designing and Implementing Big Data Analytics: Azure Pipelines and Activities Structure
After watching this video, you will be able to recognize key properties and the JSON structure of pipelines and activities in Azure Data Factory.
-
Designing and Implementing Big Data Analytics: Lambda Architecture and Azure Stream
After watching this video, you will be able to recognize the features of the Lambda architecture and the capabilities of Azure Stream.
-
Designing and Implementing Big Data Analytics: Azure Data Factory Features
After watching this video, you will be able to identify key features of Azure Data Factory.
-
Designing and Implementing Big Data Analytics: Azure Data Factory Components and Data Sources
After watching this video, you will be able to identify key components and data sources for Azure Data Factory.
-
Designing and Implementing Big Data Analytics: Azure Storage and Consumption Components
After watching this video, you will be able to recognize the Azure output storage and consumption components.
-
Designing and Implementing Big Data Analytics: Reference Data Streams
After watching this video, you will be able to design reference data streams from Blob Storage.
-
Designing and Implementing Big Data Analytics: Reference Data Sets
After watching this video, you will be able to design and configure stream reference data from Event Hubs and IoT source.
-
Designing and Implementing Big Data Analytics: Big Data Output Storage Options
After watching this video, you will be able to store and view Stream Analytics jobs.
-
Designing and Implementing Big Data Analytics: Data Visualization with Power Pivot
After watching this video, you will be able to visualize big data with Power Pivot.
-
Designing and Implementing Big Data Analytics: Data Visualization with Power View
After watching this video, you will be able to visualize big data with Power View.
-
Designing and Implementing Big Data Analytics: Visualization with SQL Server Reporting Services
After watching this video, you will be able to create custom reports with SQL Server Reporting Services.
-
Designing and Implementing Big Data Analytics: Azure Machine Learning Batch Scoring Activity
After watching this video, you will be able to use batch scoring activity.
-
Designing and Implementing Big Data Analytics: Automating Retraining with Azure PowerShell
After watching this video, you will be able to retrain a new Resource Manager using the Machine Learning Management PowerShell cmdlets.
-
Designing and Implementing Big Data Analytics: Recognize Azure Machine Learning Features
After watching this video, you will be able to recognize key concepts, features and capabilities of Azure Machine Learning.
-
Designing and Implementing Big Data Analytics: Azure Machine Learning Studio Capabilities
After watching this video, you will be able to recognize key capabilites of the Azure Machine Learning Studio.
-
Designing and Implementing Big Data Analytics: Machine Learning and the Data Factory
After watching this video, you will be able to identify key steps to creating an experiment in Azure Machine Learning Studio.
-
Designing and Implementing Big Data Analytics: Create and Publish an Experiment
After watching this video, you will be able to recognize keys steps to deploy an Azure Machine Learning web service.
-
Designing and Implementing Big Data Analytics: Retraining Experiments
After watching this video, you will be able to retrain a published experiment.
-
Designing and Implementing Big Data Analytics: Update Resource Activity
After watching this video, you will be able to recognize key aspects of updating models using the Update Resource Activity.
-
Designing and Implementing Big Data Analytics: Create a Pipeline to Transform Data
After watching this video, you will be able to create a pipeline to transform data using Azure PowerShell.
-
Designing and Implementing Big Data Analytics: Pipeline Monitoring with PowerShell
After watching this video, you will be able to monitor a pipeline with Azure PowerShell.
-
Designing and Implementing Big Data Analytics: Azure Machine Learning
After watching this video, you will be able to recognize key features of Azure Machine Learning.
-
Designing and Implementing Big Data Analytics: Azure Machine Learning Key Concepts and Terms
After watching this video, you will be able to identify key concepts and terms of Azure Machine Learning.
-
Designing and Implementing Big Data Analytics: Team Data Science Process
After watching this video, you will be able to recognize key steps in the team data science process and the process lifecycle.
-
Designing and Implementing Big Data Analytics: Azure Machine Learning Studio
After watching this video, you will be able to recognize key features of the Azure Machine Learning Studio.
-
Designing and Implementing Big Data Analytics: Azure SQL Database Linked Service
After watching this video, you will be able to create a linked service for an Azure SQL database.
-
Designing and Implementing Big Data Analytics: Create an Input Dataset
After watching this video, you will be able to create an input dataset using Azure PowerShell.
-
Designing and Implementing Big Data Analytics: Create an Output Dataset
After watching this video, you will be able to create an output dataset using Azure PowerShell.
-
Designing and Implementing Big Data Analytics: Create a Pipeline to Copy Data
After watching this video, you will be able to create a pipeline with a copy activity using Azure PowerShell.
-
Designing and Implementing Big Data Analytics: Transform Data with Pig Activity
After watching this video, you will be able to use Pig Activity to transform data.
-
Designing and Implementing Big Data Analytics: MapReduce Activity
After watching this video, you will be able to use MapReduce Activity.
-
Designing and Implementing Big Data Analytics: Stored Procedure Activity
After watching this video, you will be able to recognize how to use stored procedure activities.
-
Designing and Implementing Big Data Analytics: Custom Activities
After watching this video, you will be able to recognize how to create custom activities.
-
Designing and Implementing Big Data Analytics: Common Troubleshooting Solutions
After watching this video, you will be able to monitor and identify common problems and recognize their solutions.
-
Designing and Implementing Big Data Analytics: Create an Azure Data Factory with PowerShell
After watching this video, you will be able to create a Data Factory with Azure PowerShell.
-
Designing and Implementing Big Data Analytics: Azure Storage Account Linked Service
After watching this video, you will be able to create a linked service for an Azure storage account.
-
Designing and Implementing Big Data Analytics: Data Transformation and Prerequisites
After watching this video, you will be able to recognize methods for data transformation and prerequisites.
-
Designing and Implementing Big Data Analytics: Transform Data with a Pipeline
After watching this video, you will be able to transform data with a pipeline using Visual Studio.
-
Designing and Implementing Big Data Analytics: Transform Data with Hive Activity
After watching this video, you will be able to use Hive Activity to transform data.
-
Designing and Implementing Big Data Analytics: Diagram View
After watching this video, you will be able to navigate the Diagram View.
-
Designing and Implementing Big Data Analytics: Potential Errors and Problems
After watching this video, you will be able to identify potential errors and solutions with Data Factory.
-
Designing and Implementing Big Data Analytics: Installation and Registration Problems
After watching this video, you will be able to recognize common problems with installation and registration and possible solutions.
-
Designing and Implementing Big Data Analytics: Limited Functionality Problems
After watching this video, you will be able to recognize common problems with limited functionality and possible solutions.
-
Designing and Implementing Big Data Analytics: Troubleshoot Other Common Problems
After watching this video, you will be able to recognize other possible problems and solutions.
-
Designing and Implementing Big Data Analytics: Azure Data Factory Copy Wizard
After watching this video, you will be able to use Azure Data Factory Copy Wizard.
-
Designing and Implementing Big Data Analytics: Load Big Data
After watching this video, you will be able to recognize steps to load big data from Azure Blob Storage into Azure SQL Data Warehouse.
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Designing and Implementing Big Data Analytics: Move Data from a Source to a Sink
After watching this video, you will be able to recognize steps to copy data from on-premise to a cloud storage and from one cloud storage to another.
-
Designing and Implementing Big Data Analytics: Azure Architecture and Analytics Components
After watching this video, you will be able to recognize the Azure architecture and the various components of data sources, integration, and real-time analytics.
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Data Modeling for Hadoop: HDFS Schema Design
After watching this video, you will be able to recognize important considerations when designing HDFS schema.
-
Data Modeling for Hadoop: HBase Schema Design
After watching this video, you will be able to recognize important points when designing HDFS schema.
-
Data Modeling for Hadoop: Introduction to Data Movement in Hadoop
After watching this video, you will be able to identify basic concepts of data movement in Hadoop.
-
Data Modeling for Hadoop: Data Ingestion Considerations in Hadoop
After watching this video, you will be able to list important factors that need to be considered for importing data into Hadoop.
-
Data Modeling for Hadoop: Data Ingestion Options in Hadoop
After watching this video, you will be able to identify tools and methods for moving data into Hadoop.
-
Data Modeling for Hadoop: What is a Data Stream?
After watching this video, you will be able to recognize characteristics of a data stream.
-
Data Modeling for Hadoop: What is Data Management?
After watching this video, you will be able to define data management.
-
Data Modeling for Hadoop: Introduction to Data Modeling in Hadoop
After watching this video, you will be able to recognize important data modeling concepts in Hadoop.
-
Data Modeling for Hadoop: Hadoop Data Storage Options
After watching this video, you will be able to identify important issues for storing data in Hadoop.
-
Hadoop Ecosystem: YARN Configurations
After watching this video, you will be able to identify the standard configuration parameters to be changed for YARN.
-
Hadoop Operations: Configuring Kerberos
After watching this video, you will be able to configure Kerberos.
-
Hadoop Operations: Examining Hadoop and Kerberos
After watching this video, you will be able to describe how to configure HDFS and YARN for use with Kerberos.
-
Data Modeling for Hadoop: What is Kerberos?
After watching this video, you will be able to define Kerberos.
-
Data Modeling for Hadoop: Hadoop and Kerberos
After watching this video, you will be able to define basics of authentication in Hadoop using Kerberos.
-
Data Modeling for Hadoop: Introduction to Data Security Management
After watching this video, you will be able to define data security management and its major domains.
-
Data Modeling for Hadoop: Data Warehousing with Hadoop
After watching this video, you will be able to identify central issues in processing and management of big data.
-
Data Modeling for Hadoop: Data Modeling in Hadoop
After watching this video, you will be able to identify important points in Hadoop data modeling.
-
Big Data Ops: Big Data Architecture Trends
After watching this video, you will be able to describe big data trends and characteristics.
-
Big Data Ops: Data Warehousing vs. Big Data Testing
After watching this video, you will be able to describe key similarities and differences between data warehousing testing vs. big data testing.
-
Big Data Ops: Integrating Big Data and Business
After watching this video, you will be able to recognize the operations that integrate big data.
-
Big Data Ops: Big Data and ETL
After watching this video, you will be able to recognize the various processes of automating ETL jobs.
-
Big Data Ops: Methods of Testing Big Data Applications
After watching this video, you will be able to recognize the various testing methods and strategies.
-
Big Data Ops: ETL and Data Warehouse Testing Principles
After watching this video, you will be able to recognize the testing methods in ETL processes.
-
Big Data Ops: Performance Testing
After watching this video, you will be able to describe the various methods in performance testing.
-
Big Data Ops: Biggest Problems with Big Data Today
After watching this video, you will be able to describe the main challenges business face with big data currently.
-
Big Data Ops: Testing Big Data Loads
After watching this video, you will be able to demonstrate key strategies in big data testing.
-
Big Data Ops: Testing Big Data with Hadoop Ecosystem
After watching this video, you will be able to identify important tools available in Hadoop ecosystem for testing big data.
-
Big Data Ops: Overview of Big Data Operations
After watching this video, you will be able to describe big data and where it's heading today.
-
Big Data Ops: Monitoring Big Data Repositories
After watching this video, you will be able to describe the process of monitoring big data repositories and predictive modeling.
-
Big Data Ops: Industries in Big Data Operations
After watching this video, you will be able to recognize the trends and the various industries that are exposed to big data operations.
-
Big Data Ops: Advancements in Big Data Operations
After watching this video, you will be able to identify the technologies and advancements in big data.
-
Big Data Ops: Concepts of Big Data Network Monitoring
After watching this video, you will be able to recognize big data network monitoring operations and its importance.
-
Big Data Ops: Trends in Big Data system Orchestrations
After watching this video, you will be able to list the various software and applications that can be used to provide big data orchestrations.
-
Big Data Ops: Understanding Big Data Metrics
After watching this video, you will be able to recognize the various big data KPIs and how each can be used.
-
Big Data Ops: Monitoring Performance Issues
After watching this video, you will be able to describe the various performance issues and how to solve them using data monitoring.
-
Hadoop Installation and Maintenance: Accessibility Features
After watching this video, you will be able to list accessibility features such as FS Shell, DFSAdmin, and Browser Interface.
-
Hadoop Installation and Maintenance: Data Organization
After watching this video, you will be able to describe data organization considerations such as data blocks and replication pipelining.
-
Hadoop Installation and Maintenance: Space Reclamation
After watching this video, you will be able to describe space reclamation considerations such as file deletes and replication factors.
-
Hadoop Installation and Maintenance: Ranger Admin Tool
After watching this video, you will be able to use the Ranger Admin tool.
-
Hadoop Installation and Maintenance: Ranger Build Process
After watching this video, you will be able to build Ranger from source.
-
Hadoop Installation and Maintenance: Ranger on Hortonworks
After watching this video, you will be able to download Hortonworks Sandbox on VirtualBox to run Ranger.
-
Hadoop Installation and Maintenance: Ranger User Sync
After watching this video, you will be able to work with Ranger User Sync.
-
Hadoop Installation and Maintenance: What Is Ranger?
After watching this video, you will be able to describe the benefits of using Ranger with Hadoop and list its installation prerequisites.
-
Hadoop Installation and Maintenance: Working with the HDFS Architecture
After watching this video, you will be able to work with the HDFS architecture.
-
Hadoop Installation and Maintenance: Command Line EMR Script Processing
After watching this video, you will be able to run scripts in EMR from the command line using AWS CLI.
-
Hadoop Installation and Maintenance: Command Line EMR Data Input
After watching this video, you will be able to use AWS CLI to upload data to S3 for EMR.
-
Hadoop Installation and Maintenance: Ranger Hive Permissions
After watching this video, you will be able to modify an access policy for Hive queries using Ranger.
-
Hadoop Installation and Maintenance: Ranger Admin Setting Customization
After watching this video, you will be able to customize Ranger Admin settings.
-
Hadoop Installation and Maintenance: Ranger REST API
After watching this video, you will be able to query the Ranger REST API.
-
Hadoop Installation and Maintenance: Node Removal
After watching this video, you will be able to remove a node from a Hadoop cluster.
-
Hadoop Installation and Maintenance: Filesystem Balancer
After watching this video, you will be able to use the filesystem balancer tool to keep filesystem datanodes evenly balanced.
-
Hadoop Installation and Maintenance: Cloudera
After watching this video, you will be able to list the components of a Cloudera distribution, including Impala, Crunch, Kite, and Cloudera Manager.
-
Hadoop Installation and Maintenance: Hadoop Distributions
After watching this video, you will be able to describe the benefits of distributions.
-
Hadoop Installation and Maintenance: Backups
After watching this video, you will be able to demonstrate how to perform metadata and data backups.
-
Hadoop Installation and Maintenance: Securing Hadoop using Ranger
After watching this video, you will be able to modify and query a policy in Ranger.
-
Hadoop Installation and Maintenance: Common Hadoop Issues
After watching this video, you will be able to list common problems for Hadoop administrators.
-
Hadoop Installation and Maintenance: HDFS Snapshots and Distributed Copies
After watching this video, you will be able to create and delete snapshots.
-
Hadoop Installation and Maintenance: Hortonworks
After watching this video, you will be able to name the components of a Hortonworks distribution, including Tez, Falcon, and Ambari.
-
Hadoop Installation and Maintenance: Hadoop Snapshots
After watching this video, you will be able to perform Hadoop snapshot operations.
-
Hadoop Installation and Maintenance: MapR
After watching this video, you will be able to recall the benefits of the MapR distribution.
-
Hadoop Installation and Maintenance: Format HDFS
After watching this video, you will be able to format HDFS and configure common options.
-
Hadoop Installation and Maintenance: Expand a Hadoop Cluster
After watching this video, you will be able to add a new node to an existing Hadoop cluster.
-
Hadoop Installation and Maintenance: Working with Clusters in Hadoop
After watching this video, you will be able to start a Hadoop cluster and run a mapreduce job.
-
Hadoop Installation and Maintenance: Hadoop MapReduce Job
After watching this video, you will be able to run an example mapreduce job to perform a word count.
-
Hadoop Installation and Maintenance: Single Node Cluster
After watching this video, you will be able to set up Hadoop on a single node.
-
Hadoop Installation and Maintenance: Prepare Ubuntu for Hadoop
After watching this video, you will be able to configure an Ubuntu server for ssh and Java for Hadoop.
-
Hadoop Installation and Maintenance: Cluster Configurations
After watching this video, you will be able to describe the different cluster configurations, including single-rack deployments, three-rack deployments, and large-scale deployments.
-
Hadoop Installation and Maintenance: Small Multinode Cluster
After watching this video, you will be able to set up Hadoop on four nodes.
-
Hadoop Installation and Maintenance: Hadoop on EMR
After watching this video, you will be able to describe the benefits of using Apache Hadoop on Amazon EMR.
-
Hadoop Installation and Maintenance: EMRFS Configuration
After watching this video, you will be able to describe the EMR File System configuration.
-
Hadoop Installation and Maintenance: EMR Configuration
After watching this video, you will be able to configure an initial EMR setup in AWS.
-
Hadoop Installation and Maintenance: Working with Amazon EMR
After watching this video, you will be able to use Hadoop on Amazon EMR.
-
Hadoop Installation and Maintenance: EMR Data
After watching this video, you will be able to prepare data for use in EMR.
-
Hadoop Installation and Maintenance: EMR Cluster Launch
After watching this video, you will be able to launch a small EMR cluster.
-
Hadoop Installation and Maintenance: EMR Environment
After watching this video, you will be able to reset an EMR environment.
-
Hadoop Installation and Maintenance: EMR Script Processing
After watching this video, you will be able to run scripts in a cluster using Amazon EMR.
-
Hadoop Installation and Maintenance: Data Replication
After watching this video, you will be able to provide an overview of data replication.
-
Hadoop Installation and Maintenance: File System Namespace
After watching this video, you will be able to describe the file system namespace.
-
Hadoop Installation and Maintenance: Communication Protocols
After watching this video, you will be able to describe the various HDFS communication protocols.
-
Hadoop Installation and Maintenance: Robustness
After watching this video, you will be able to list considerations relating to robustness.
-
Hadoop Installation and Maintenance: HDFS Architecture
After watching this video, you will be able to provide an overview of the HDFS architecture and its main building blocks.
-
Hadoop Installation and Maintenance: NameNode and DataNodes
After watching this video, you will be able to describe NameNode and DataNodes in HDFS.
-
Hadoop Installation and Maintenance: HDFS Considerations
After watching this video, you will be able to list considerations for the HDFS architecture, such as hardware failure, large data sets, and the coherency model.
-
Big Data Ops: Test Big Data
After watching this video, you will be able to describe important characteristics of big data testing.
-
Big Data Ops: Prevention and Preserving
After watching this video, you will be able to recognize the various ways to keep data clean, maintained and secure.
-
Big Data Ops: Data Leakage
After watching this video, you will be able to describe data leakage and ways to prevent it.
-
Data Modeling for Hadoop: Practice Filtering in Hadoop
After watching this video, you will be able to filter information in Hadoop.
-
Data Modeling for Hadoop: Features of Hadoop Application
After watching this video, you will be able to recognize Spark and its benefits over traditional MapReduce.
-
Data Modeling for Hadoop: Hadoop Distributions
After watching this video, you will be able to identify available commercial distributions for Hadoop.
-
Data Modeling for Hadoop: Java MapReduce
After watching this video, you will be able to use code that runs on Hadoop.
-
Data Modeling for Hadoop: MapReduce Examples
After watching this video, you will be able to identify how MapReduce processes information.
-
Data Modeling for Hadoop: What Is MapReduce?
After watching this video, you will be able to define basics of MapReduce.
-
Data Modeling for Hadoop: What Is YARN?
After watching this video, you will be able to recognize basics of YARN.
-
Data Modeling for Hadoop: HDFS Commands
After watching this video, you will be able to use HDFS.
-
Data Modeling for Hadoop: Read/Write Processes in HDFS
After watching this video, you will be able to recognize how to read and write in HDFS.
-
Data Modeling for Hadoop: Internals of HDFS
After watching this video, you will be able to define HDFS components.
-
Data Modeling for Hadoop: Hadoop Deployment
After watching this video, you will be able to identify Hadoop components.
-
Data Modeling for Hadoop: Hadoop Data Type
After watching this video, you will be able to recognize storing and modeling data in Hadoop.
-
Data Modeling for Hadoop: Hadoop Ecosystem (Sqoop, Flume, Mahout, Oozie)
After watching this video, you will be able to define Sqoop, Flume, Mahout, and Oozie.
-
Data Modeling for Hadoop: Hadoop Ecosystem (Pig, HIVE, HBASE)
After watching this video, you will be able to define Pig, HIVE, and HBASE.
-
Data Modeling for Hadoop: What Is Hadoop?
After watching this video, you will be able to recognize basics of Hadoop, history, milestones, and core components.
-
Data Modeling for Hadoop: Big Data Platforms
After watching this video, you will be able to identify big data infrastructure issues, and explain benefits of Hadoop.
-
Data Modeling for Hadoop: What Is Big Data and Its Applications?
After watching this video, you will be able to recognize what big data is, sources and types of data, evolution and characteristics of big data, and use cases of big data.
-
Data Modeling for Hadoop: Virtual machine Setup
After watching this video, you will be able to set up a virtual machine.
-
Data Modeling for Hadoop: Unix Commands
After watching this video, you will be able to recognize basic and most useful Unix commands.
-
Data Modeling for Hadoop: Install Linux
After watching this video, you will be able to install Linux on a virtual machine.
-
Big Data with Apache Spark: Deduplication
After watching this video, you will be able to implement deduplication with and without watermarking.
-
Big Data with Apache Spark: Continuous Applications
After watching this video, you will be able to describe continuous applications in terms of structured streaming.
-
Big Data with Apache Spark: Windowing
After watching this video, you will be able to apply window operations on event time.
-
Big Data with Apache Spark: Stream Output
After watching this video, you will be able to write stream data using writeStream.
-
Big Data with Apache Spark: Streaming Query Manager
After watching this video, you will be able to manage streaming queries.
-
Big Data with Apache Spark: Streaming Query
After watching this video, you will be able to use streaming query objects.
-
Big Data with Apache Spark: File Sinks
After watching this video, you will be able to store stream output to a directory using a file sink.
-
Big Data with Apache Spark: Environment Configuration
After watching this video, you will be able to use the Spark environment configuration parameters.
-
Big Data with Apache Spark: Web UI
After watching this video, you will be able to access the web user interface.
-
Big Data with Apache Spark: Word Count
After watching this video, you will be able to use structured streaming to implement a word count on a text stream.
-
Big Data with Apache Spark: Checkpointing
After watching this video, you will be able to enable checkpointing in structured streaming.
-
Big Data with Apache Spark: Speculation
After watching this video, you will be able to modify speculation controls for Spark tasks.
-
Big Data with Apache Spark: Memory Allocation
After watching this video, you will be able to set JVM fractional memory amounts for Spark.
-
Big Data with Apache Spark: REST API
After watching this video, you will be able to use JSON to query monitoring tools for Spark.
-
Big Data with Apache Spark: Identify Spark Streaming Basics
After watching this video, you will be able to describe the basics of Spark Streaming.
-
Big Data with Apache Spark: Memory Tuning
After watching this video, you will be able to describe memory management and consumption.
-
Big Data with Apache Spark: Serialization
After watching this video, you will be able to describe data serialization and the role it plays in the performance of Spark applications.
-
Big Data with Apache Spark: Downloading and Installing Apache Spark on Mac OS
After watching this video, you will be able to download and install Apache Spark 2.2 on Mac OS.
-
Big Data with Apache Spark: Downloading and Installing Apache Spark
After watching this video, you will be able to download and install Apache Spark 2.2.
-
Big Data with Apache Spark: Overview of Apache Spark
After watching this video, you will be able to describe Apache Spark 2.2 and the main components of a Spark application.
-
Big Data with Apache Spark: Working with Spark Shell
After watching this video, you will be able to use Spark shell for analyzing data interactively.
-
Big Data with Apache Spark: Building Spark
After watching this video, you will be able to build Apache Spark using Apache Maven.
-
Big Data with Apache Spark: Linking to Spark
After watching this video, you will be able to link an application to Spark.
-
Big Data with Apache Spark: Use Apache Spark Shell
After watching this video, you will be able to use Apache Spark shell.
-
Big Data with Apache Spark: Running Spark on Clusters
After watching this video, you will be able to describe how Spark runs on clusters and the three supported cluster managers.
-
Big Data with Apache Spark: Initializing Apache Spark
After watching this video, you will be able to create a SparkContext to initialize Apache Spark.
-
Big Data with Apache Spark: Spark Configuration
After watching this video, you will be able to identify the three locations Spark provides to configure the system.
-
Big Data with Apache Spark: Aggregations
After watching this video, you will be able to use aggregations with the built-in DataFrames functions.
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Big Data with Apache Spark: DataFrames
After watching this video, you will be able to create DataFrames with Spark SQL.
-
Big Data with Apache Spark: SparkSession
After watching this video, you will be able to create a SparkSession.
-
Big Data with Apache Spark: Apache Spark SQL Overview
After watching this video, you will be able to describe Apache Spark SQL.
-
Big Data with Apache Spark: SQL Queries
After watching this video, you will be able to run SQL queries programmatically.
-
Big Data with Apache Spark: Datasets
After watching this video, you will be able to create Datasets with Spark SQL.
-
Big Data with Apache Spark: Temporary View
After watching this video, you will be able to create a global temporary view.
-
Big Data with Apache Spark: Querying with SQL
After watching this video, you will be able to run SQL directly on files.
-
Big Data with Apache Spark: Specifying a Data Source
After watching this video, you will be able to manually specify a data source.
-
Big Data with Apache Spark: Load/Save Functions
After watching this video, you will be able to use Load/Save functions.
-
Big Data with Apache Spark: JSON Datasets
After watching this video, you will be able to use JSON Datasets with Spark SQL.
-
Big Data with Apache Spark: Persistent Tables
After watching this video, you will be able to use Spark SQL to save a DataFrame as a persistent table.
-
Big Data with Apache Spark: Parquet Files
After watching this video, you will be able to write parquet files with Spark SQL.
-
Big Data with Apache Spark: SaveMode
After watching this video, you will be able to use SaveMode to handle save operations.
-
Big Data with Apache Spark: Use Spark SQL
After watching this video, you will be able to use Spark SQL to create Datasets and DataFrames.
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Big Data with Apache Spark: Stream Input
After watching this video, you will be able to read stream input using readStream.
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Big Data with Apache Spark: Structured Streaming Overview
After watching this video, you will be able to describe Structured Streaming.
-
Big Data with Apache Spark: Partitioning
After watching this video, you will be able to use partitioning when saving persistent tables.
-
Big Data with Apache Spark: Garbage Collection Tuning
After watching this video, you will be able to describe garbage collection tuning.
-
Big Data with Apache Spark: Executor Memory
After watching this video, you will be able to determine executor memory allocation.
-
Big Data with Apache Spark: Data Compression
After watching this video, you will be able to implement data compression on parquet storage.
-
Big Data with Apache Spark: Explain Query Execution
After watching this video, you will be able to use query execution plan explainer.
-
Big Data with Apache Spark: Broadcast Functionality
After watching this video, you will be able to use the broadcast functionality.
-
Big Data with Apache Spark: Parallelism
After watching this video, you will be able to set the level of parallelism.
-
Big Data with Apache Spark: SSL Settings
After watching this video, you will be able to configure the SSL settings.
-
Big Data with Apache Spark: Secure Event Logs
After watching this video, you will be able to set permissions on the directory where the event logs are stored.
-
Big Data with Apache Spark: Spark UI
After watching this video, you will be able to secure the Spark UI by limiting access using a firewall.
-
Big Data with Apache Spark - Exercise: Monitor Spark Applications
After watching this video, you will be able to monitor Spark applications.
-
Big Data with Apache Spark: SASL Encryption
After watching this video, you will be able to enable SASL encryption for a Spark application.
-
Big Data with Apache Spark: YARN Deployments
After watching this video, you will be able to configure spark.authenticate.
-
Big Data with Apache Spark: Shared Secret
After watching this video, you will be able to configure a shared secret for Spark authentication.
-
Big Data with Apache Spark: Configure Spark Security
After watching this video, you will be able to configure Spark security.
-
Big Data with Apache Spark: Network Security
After watching this video, you will be able to configure the primary ports Spark uses for communication.
-
Apache Kafka: Using Kafka, Spark, and Storm Together
After watching this video, you will be able to use Kafka, Spark, and Storm in a pipeline to process words in a sentence.
-
Apache Kafka: Create Real-time Kafka Applications
After watching this video, you will be able to create a simple real-time application using Kafka, Spark, and Storm.
-
Apache Kafka: Using Spark to Process Tweets
After watching this video, you will be able to use Spark to process tweets and take action on specific words.
-
Apache Kafka: Creating a Consumer to Transfer to Spark
After watching this video, you will be able to create a Consumer to transfer tweets to Spark.
-
Apache Kafka: Using Storm to Process Tweets
After watching this video, you will be able to use Storm to process the words in incoming tweets.
-
Apache Kafka: Creating a Consumer to Transfer to Storm
After watching this video, you will be able to create a Consumer to transfer tweets to Storm.
-
Apache Kafka: The Real-time Capabilities of Kafka
After watching this video, you will be able to describe the real-time capabilities of Kafka.
-
Apache Kafka: Balancing Leadership and Unclean Leader Election
After watching this video, you will be able to set preferred replicas for leadership and handle unclean leader elections.
-
Apache Kafka: Creating a Producer Using the Twitter API
After watching this video, you will be able to create a Kafka Producer to process tweets.
-
Apache Kafka: Installing the Twitter Streaming API
After watching this video, you will be able to install and set up the Twitter Streaming API and framework to create a real-time Twitter application.
-
Apache Kafka: Working with Rack Awareness for Replication
After watching this video, you will be able to balance replicas across zones or racks to prevent rack-failure data loss.
-
Apache Kafka: Mirroring between Clusters
After watching this video, you will be able to mirror data between clusters such as between two data centers.
-
IBM BigInsights Fundamentals: Creating a Text Analytics Project
After watching this video, you will be able to recognize how to create a text analytics project, add documents, and use and customize a prebuilt extractor.
-
IBM BigInsights Fundamentals: IBM BigInsights' Requirements
After watching this video, you will be able to choose between IBM BigInsights' software installation packages based on a scenario, and ensure the system requirements are met.
-
IBM BigInsights Fundamentals: Creating Master Workbooks Using BigSheets
After watching this video, you will be able to identity the steps required to create master workbooks.
-
IBM BigInsights Fundamentals: Finalizing Extractors
After watching this video, you will be able to complete the steps required to finalize and save an extractor.
-
IBM BigInsights Fundamentals: Creating Extractors
After watching this video, you will be able to identify the steps required to configure extractors and extract of features.
-
IBM BigInsights Fundamentals: Analyzing Input Documents
After watching this video, you will be able to recognize how to analyze input documents to identify information that will be extracted.
-
IBM BigInsights Fundamentals: Exploring BigInsights' Apache Hadoop Requirements
After watching this video, you will be able to identify the system requirements for IBM BigInsights for Apache Hadoop.
-
IBM BigInsights Fundamentals: Integrating BigInsights with other IBM Products
After watching this video, you will be able to recognize that IBM BigInsights can be integrated with other IBM products.
-
IBM BigInsights Fundamentals: Exploring BigInsights' Value-add Services
After watching this video, you will be able to recognize the IBM open platform with Apache Hadoop and the BigInsights value-add services, including the BigInsights package editions and their supported features.
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IBM BigInsights Fundamentals: Exploring the Apache Open-source Projects
After watching this video, you will be able to identify the data access, search, administration, and security Apache open-source projects.
-
IBM BigInsights Fundamentals: Exploring the IBM Hadoop Solution Layers
After watching this video, you will be able to identify the layers of IBM's Hadoop solution.
-
IBM BigInsights Fundamentals: Exploring what IBM Hadoop Offers
After watching this video, you will be able to identify the IBM Hadoop offerings.
-
IBM BigInsights Fundamentals: Identifying the Open-source Technologies Used
After watching this video, you will be able to recognize the open source technologies that are used with IBM Open platform with Apache Hadoop.
-
IBM BigInsights Fundamentals: Exploring IBM BigInsights' Hadoop Capabilities
After watching this video, you will be able to recognize the software capabilities of IBM BigInsights for Apache Hadoop.
-
IBM BigInsights Fundamentals: Exploring IBM Open Platform with Apache Hadoop
After watching this video, you will be able to recognize the IBM open platform with Apache Hadoop benefits.
-
IBM BigInsights Fundamentals: Getting to Know IBM BigInsights
After watching this video, you will be able to recognize what IBM BigInsights is.
-
IBM BigInsights Fundamentals: Exploring the Components of Hadoop
After watching this video, you will be able to recognize the core components that make up Hadoop.
-
IBM BigInsights Fundamentals: What Is Hadoop?
After watching this video, you will be able to recognize what Hadoop is.
-
IBM BigInsights Fundamentals: Using BigInsights on Cloud
After watching this video, you will be able to identify how IBM BigInsights can be used using the cloud, including using IBM Analytics for Hadoop on Bluemix for cloud trial.
-
IBM BigInsights Fundamentals: Exploring Hadoop-as-a-service
After watching this video, you will be able to recognize what IBM BigInsights on cloud is and what it offers.
-
IBM BigInsights Fundamentals: Exploring BigSheets
After watching this video, you will be able to recognize what BigSheets is and its functions.
-
IBM BigInsights Fundamentals: Exploring IBM Big SQL
After watching this video, you will be able to Identify what IBM Big SQL is and its functions.
-
IBM BigInsights Fundamentals: Exploring BigInsights' Text Analytics
After watching this video, you will be able to recognize what BigInsights Text Analytics is and the features it offers.
-
IBM BigInsights Fundamentals: Identifying the File System
After watching this video, you will be able to recognize the IBM Open Platform with Apache Hadoop file system.
-
IBM BigInsights Fundamentals: Exploring IBM BigInsights' Quick Start Edition
After watching this video, you will be able to identify what IBM BigInsights quick start edition is and the features it offers.
-
IBM BigInsights Fundamentals: Exploring IBM BigInsights' On-premises Requirements
After watching this video, you will be able to identify the prerequisites, system requirements, the available docker images, and native install options used to install BigInsights on-premises for the IBM open platform with Apache Hadoop.
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IBM BigInsights Fundamentals: Exploring BigInsights' Use Cases
After watching this video, you will be able to recognize use cases for Hadoop and bit data analytics.
-
IBM BigInsights Fundamentals: Creating a Workbook and Exporting the Data
After watching this video, you will be able to use BigSheets to create a master workbook and export the data from the workbook.
-
IBM BigInsights Fundamentals: Creating Bar Charts to Visualize Data Using Big R
After watching this video, you will be able to create a bar chart to visualize big data with IBM BigInsights Big R.
-
IBM BigInsights Fundamentals: Using IBM BigInsights Big R to Analyze Data
After watching this video, you will be able to work with IBM BIgInsights Big R to analyze data.
-
IBM BigInsights Fundamentals: Creating Simple Queries to Analyze Data
After watching this video, you will be able to complete the steps required to create and run a query to analyze data.
-
IBM BigInsights Fundamentals: Configuring an SQL Script File
After watching this video, you will be able to recognize the steps required to configure an SQL script file.
-
IBM BigInsights Fundamentals: Exporting Workbook Data
After watching this video, you will be able to identify the steps required to export data from workbooks to a CSV file and a browser tab.
-
IBM BigInsights Fundamentals: Using IBM BigInsights Big R to Explore a Dataset
After watching this video, you will be able to recognize how to use IBM BigInsights Big R to explore the structure of a dataset.
-
IBM BigInsights Fundamentals: Uploading a Dataset to BigInsights Server
After watching this video, you will be able to perform the steps required to upload a dataset to the BigInsights server using Big R.
-
IBM BigInsights Fundamentals: Configuring a Big SQL Query to Analyze Data
After watching this video, you will be able to complete the steps to configure and run an Big SQL query to analyze data.
-
IBM BigInsights Fundamentals: Creating Views to Represent Data
After watching this video, you will be able to identify the steps required to create a view that will represent data from a query.
-
IBM BigInsights Fundamentals: Using BigSheet Diagrams to View Data
After watching this video, you will be able to complete the steps required to view data in BigSheets diagrams.
-
IBM BigInsights Fundamentals: Grouping Data in BigSheets
After watching this video, you will be able to complete the steps required to create columns based on grouped data.
-
IBM BigInsights Fundamentals: Merging Data Using BigSheets
After watching this video, you will be able to combine data from two workbooks into a single data collection using BigSheets.
-
IBM BigInsights Fundamentals: Creating Child Workbooks Using BigSheets
After watching this video, you will be able to recognize the steps required to create child workbooks.
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Apache Solr Essentials: Field Facets
After watching this video, you will be able to create, run, and examine field-based facet queries.
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Apache Solr Essentials: Facet-based Searching
After watching this video, you will be able to describe facet-based searching using Solr.
-
Apache Solr Essentials: Pivot Facets
After watching this video, you will be able to create, run, and examine a pivot-based facet query.
-
Apache Solr Essentials: Number Range Facets
After watching this video, you will be able to create, run, and examine number range-based facet queries.
-
Apache Solr Essentials: Solr Data Import Handler
After watching this video, you will be able to describe structured data import to Apache Solr.
-
Apache Solr Essentials: Geo-spatial Facets
After watching this video, you will be able to create, run, and examine a geo-special facet.
-
Apache Solr Essentials: Solr Search (Query) via the Administration Console
After watching this video, you will be able to search for items in Solr via the Solr Administration console (portal).
-
Apache Solr Essentials: Solr Search (Query)
After watching this video, you will be able to query Solr data using command line utilities or browsers using RESTful request.
-
Apache Solr Essentials: Combined Searches
After watching this video, you will be able to search for two or more items.
-
Apache Solr Essentials: Phrase Searching
After watching this video, you will be able to search for items using phrases.
-
Apache Solr Essentials: Creating a DIH Configuration File
After watching this video, you will be able to create a DIH configuration file.
-
Apache Solr Essentials: SolrCloud Architecture
After watching this video, you will be able to describe the architecture of a SolrCloud deployment.
-
Apache Solr Essentials: Working with Databases
After watching this video, you will be able to import structured data to a Solr index from a database.
-
Apache Solr Essentials: Manage and Query Apache Solr
After watching this video, you will be able to work with data in Apache solr, query the index with simple and faceted techniques, and work with Apache SolrCloud.
-
Apache Solr Essentials: Working with SolrCloud
After watching this video, you will be able to Work with a SolrCloud deployment.
-
Apache Solr Essentials: Download and Install Apache Solr
After watching this video, you will be able to download, install, and run an Apache Solr server instance.
-
Apache Solr Essentials: Apache Solr
After watching this video, you will be able to describe the Apache Solr product.
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Apache Solr Essentials: The Solr Service
After watching this video, you will be able to manage the Solr service – start, stop, check if running, the help system, port, etc..
-
Apache Solr Essentials: Distribution Architecture
After watching this video, you will be able to examine and navigate the Solr distribution folder structure and the example sub folder.
-
Apache Solr Essentials: The Solr Data Model
After watching this video, you will be able to examine Solr documents and fields.
-
Apache Solr Essentials: Solr Field Types
After watching this video, you will be able to describe the supported field types.
-
Apache Solr Essentials: Solr Field Types and Field Type Properties
After watching this video, you will be able to describe solr fields, field properties, and field types.
-
Apache Solr Essentials: Field Analysis and Index Control
After watching this video, you will be able to describe field analysis and index control via analyzers, tokenizers and filters; describe available tokenizer and filters.
-
Apache Solr Essentials: Solr Fields and Field Properties
After watching this video, you will be able to describe Solr Fields and field definition; describe copyField and dynamicField.
-
Apache Solr Essentials: Solr Deployment Architecture
After watching this video, you will be able to describe the structure and components of a Solr instance directory: Solr home – cores, schema, configuration.
-
Apache Solr Essentials: Solr Administration Console
After watching this video, you will be able to activate and navigate the utilities within the Solr Administration portal.
-
Apache Solr Essentials: The Solr Schema
After watching this video, you will be able to examine the structure of a Solr core schema file – schema.xml.
-
Apache Solr Essentials: The Solr Home
After watching this video, you will be able to navigate the Solr home directory and examine the Solr instance directory.
-
Apache Solr Essentials: Work with the Sample techproducts Collection
After watching this video, you will be able to run the solr script to activate the techproducts example, and try out the search (query tool) feature of the Solr Console (portal).
-
Apache Solr Essentials: The Solr Configuration
After watching this video, you will be able to examine the structure of a Solr core configuration file – solrconfig.xml.
-
Apache Solr Essentials: Indexing CSV Data to Solr
After watching this video, you will be able to add CSV data to the solr index.
-
Apache Solr Essentials: Indexing JSON Data to Solr
After watching this video, you will be able to add JSON data to the Solr index.
-
Apache Solr Essentials: Logging
After watching this video, you will be able to configure logging via the Solr Administration console.
-
Apache Solr Essentials: Documents and Data
After watching this video, you will be able to work with documents and import data via the Solr Administration console.
-
Apache Solr Essentials: Updating Solr Data
After watching this video, you will be able to deploy, configure, and manage an Apache Solr instance, import and control data ingress to Solr core index.
-
Apache Solr Essentials: Schema
After watching this video, you will be able to work with a core's schema via the Solr Administration console.
-
Apache Solr Essentials: Create a Solr Core
After watching this video, you will be able to manually create a Solr core from scratch.
-
Apache Solr Essentials: Testing Field Types Analysis
After watching this video, you will be able to apply the Solr console analyzer utility to test fields.
-
Apache Solr Essentials: Solr Index
After watching this video, you will be able to describe indexing in Solr.
-
Apache Solr Essentials: Create and Modify Core's Schema
After watching this video, you will be able to create and modify a core's schema to implement analysis and indexing controls on incoming data.
-
Apache Solr Essentials: Indexing XML Data to Solr
After watching this video, you will be able to add XLM data to the Solr index.
-
Apache Solr Essentials: Deleting Items from the Solr
After watching this video, you will be able to remove data object from a Solr.
-
Apache Solr Essentials: Updating Solr Data
After watching this video, you will be able to update existing collection data.
-
Apache Solr Essentials: Solr Search Parameters
After watching this video, you will be able to describe Solr query parameters.
-
Apache Solr Essentials: Solr Search
After watching this video, you will be able to describe the components of Solr search engine.
-
Apache Storm Introduction: Exploring Automation Using Puppet
After watching this video, you will be able to describe the Puppet architecture and some key framework components.
-
Apache Storm Introduction: Integrating Kafka with Storm
After watching this video, you will be able to consume Kafka messages in a Storm topology.
-
Apache Storm Introduction: Storm and HBase/Redis Integration
After watching this video, you will be able to describe how HBase and Redis can be integrated and used as datastores with Apache Storm.
-
Apache Storm Introduction: Integrating JMX and Ganglia
After watching this video, you will be able to describe how JMX and Ganglia can be integrated and used to monitor Storm clusters.
-
Apache Storm Introduction: Installing Apache Hadoop
After watching this video, you will be able to download and install Apache Hadoop on a development machine.
-
Apache Storm Introduction: Analyzing an Apache Storm Cluster
After watching this video, you will be able to launch a Storm topology to a local cluster and view cluster activity in the Storm UI.
-
Apache Storm Introduction: Deploying a Trident Topology to a Cluster
After watching this video, you will be able to deploy a Trident topology to a Storm cluster.
-
Apache Storm Introduction: Setting Up a Maven Project for Thrift Client
After watching this video, you will be able to set up a Maven project in Eclipse IDE that can be used to write Java client code for connecting to a Nimbus Thrift server.
-
Apache Storm Introduction: Adding and Executing Thrift Client Code
After watching this video, you will be able to write Java client code that connects to a Nimbus Thrift server and retrieves Storm cluster statistics.
-
Apache Storm Introduction: Nimbus Analytics and its Use with Apache Storm
After watching this video, you will be able to describe the process of using the Nimbus Thrift client for obtaining Storm cluster metrics.
-
Apache Storm Introduction: Analyzing a Topology Using the Apache Storm UI
After watching this video, you will be able to analyze a Storm topology using the Storm UI.
-
Apache Storm Introduction: Using JMX to Gather Storm Metrics
After watching this video, you will be able to integrate and use JMX in Storm to obtain Storm Nimbus and Supervisor metrics.
-
Apache Storm Introduction: Configuring and Implementing Apache Storm
After watching this video, you will be able to demonstrate increased knowledge of configuring and installing Apache Storm.
-
Apache Storm Introduction: Working with Kafka Producers and Consumers
After watching this video, you will be able to produce and consume a Kafka topic.
-
Apache Kafka: Working with Log Compaction
After watching this video, you will be able to describe how log compaction works.
-
Apache Kafka: Configuring Replication
After watching this video, you will be able to configure and balance logs and leadership for replication.
-
Apache Kafka: Increasing the Replication Factor
After watching this video, you will be able to increase the replication factor for a topic.
-
Apache Kafka: Configuring the Log Cleaner
After watching this video, you will be able to configure and use the log cleaner.
-
Apache Kafka: Using Storm With Kafka
After watching this video, you will be able to describe how Kafka can be integrated with Storm.
-
Apache Kafka: Managing Offsets in Storm
After watching this video, you will be able to manage how Storm uses offsets in a Kafka-Storm application.
-
Apache Kafka: Working with a Multi-broker Cluster
After watching this video, you will be able to set up and configure a multi-broker cluster.
-
Apache Kafka: Identifying the Benefits of Kafka Clustering
After watching this video, you will be able to describe the availability and durability guarantees of Apache Kafka.
-
Apache Kafka: Expanding a Cluster
After watching this video, you will be able to add servers to a Kafka cluster and migrate data to the new servers.
-
Apache Kafka: Creating a Kafka-Storm Word Counting Application
After watching this video, you will be able to create a simple Kafka-Storm wordcount application.
-
Apache Kafka: Configuring Storm and Kafka
After watching this video, you will be able to install and configure Storm and Kafka.
-
Apache Kafka: Creating a Kafka-Spark Word-splitting Application
After watching this video, you will be able to use Kafka and Spark to split words from sentences.
-
Apache Kafka: Configuring and Using the KafkaSpout API
After watching this video, you will be able to configure the Kafka Spout for adding stream data into Storm.
-
Apache Kafka: Describing the Storm-Kafka Pipeline
After watching this video, you will be able to describe the Storm-Kafka pipeline for processing events.
-
Apache Kafka: Planning for Location and Consumer Strategies
After watching this video, you will be able to use LocationStrategies and ConsumerStrategies to improve performance.
-
Apache Kafka: Creating a Direct Stream
After watching this video, you will be able to create a direct stream to access Kafka data from Spark.
-
Apache Kafka: Managing Offsets
After watching this video, you will be able to use offsets to handle exactly-once semantics.
-
Apache Kafka: Using an RDD
After watching this video, you will be able to use an RDD in cases where batch processing would be a better solution.
-
Apache Kafka: Writing to Kafka
After watching this video, you will be able to write data back to Kafka from a Storm Bolt.
-
Apache Kafka: Reading from Kafka Using the Storm Connector
After watching this video, you will be able to read data from Kafka into Storm.
-
Apache Kafka: Reading Data from Kafka
After watching this video, you will be able to read data into Spark from Kafka.
-
Apache Kafka: Configuring Spark and Kafka
After watching this video, you will be able to install and configure the Spark Streaming package for Kafka.
-
Apache Kafka: Writing Data to Kafka
After watching this video, you will be able to write data back to Kafka from Spark.
-
Apache Kafka: Reading Data from Kafka in Parallel
After watching this video, you will be able to read data in parallel into Spark from Kafka.
-
Apache Kafka: Writing Data to Kafka in Parallel
After watching this video, you will be able to write data back to Kafka from Spark in parallel.