Participants will navigate the Hadoop ecosystem, learning topics such as:
- The internals of MapReduce and HDFS and how to write MapReduce code
- Best practices for Hadoop development, debugging, and implementation of workflows and common algorithms
- How to leverage Hive, Pig, Sqoop, Flume, Oozie, and other Hadoop ecosystem projects
- Creating custom components such as WritableComparables and InputFormats to manage complex data types
- Writing and executing joins to link data sets in MapReduce
- Advanced Hadoop API topics required for real-world data analysis
Subscription Details
This OnDemand offering provides you with a 180-day subscription that begins on the date of purchase. While the subscription is active, you will have unlimited access to the course training materials which includes recorded course lectures and demonstrations, assessment components, and hands-on exercise instructions. You will also receive 20 runtime hours of access to the online hands-on exercise environment accessible though web browser. You can start the exercise environment when you are ready to use it. You can stop or pause it when you are done for the time being, then return anytime to continue where you left off. The exercise environment remains accessible until you have used the runtime hours or the subscription period ends, whichever occurs first.
The Motivation for Hadoop
- Problems with Traditional
- Large-Scale Systems
- Introducing Hadoop
- Hadoopable Problems
Hadoop: Basic Concepts and HDFS
- The Hadoop Project and
- Hadoop Components
- The Hadoop Distributed File System
Introduction to MapReduce
- MapReduce Overview
- Example: WordCount
- Mappers
- Reducers
Hadoop Clusters and the Hadoop Ecosystem
- Hadoop Cluster Overview
- Hadoop Jobs and Tasks
- Other Hadoop Ecosystem Components
Writing a MapReduce Program in Java
- Basic MapReduce API Concepts
- Writing MapReduce Drivers, Mappers, and Reducers in Java
- Speeding Up Hadoop Development by Using Eclipse
- Differences Between the Old and New MapReduce APIs
Writing a MapReduce Program Using Streaming
- Writing Mappers and Reducers with the Streaming API
Unit Testing MapReduce Programs
- Unit Testing
- The JUnit and MRUnit Testing Frameworks
- Writing Unit Tests with MRUnit
- Running Unit Tests
Delving Deeper into the Hadoop API
- Using the ToolRunner Class
- Setting Up and Tearing Down Mappers and Reducers
- Decreasing the Amount of Intermediate Data with Combiners
- Accessing HDFS Programmatically
- Using The Distributed Cache
- Using the Hadoop API’s Library of Mappers, Reducers, and Partitioners
Practical Development Tips and Techniques
- Strategies for Debugging MapReduce Code
- Testing MapReduce Code Locally by Using LocalJobRunner
- Writing and Viewing Log Files
- Retrieving Job Information with Counters
- Reusing Objects
- Creating Map-Only MapReduce Jobs
Partitioners and Reducers
- How Partitioners and Reducers Work Together
- Determining the Optimal Number of Reducers for a Job
- Writing Customer Partitioners
Data Input and Output
- Creating Custom Writable and WritableComparable Implementations
- Saving Binary Data Using SequenceFile and Avro Data Files
- Issues to Consider When Using File Compression
- Implementing Custom InputFormats and OutputFormats
Common MapReduce Algorithms
- Sorting and Searching Large Data Sets
- Indexing Data
- Computing Term Frequency — Inverse Document Frequency
- Calculating Word Co-Occurrence
- Performing Secondary Sort
Joining Data Sets in MapReduce Jobs
- Writing a Map-Side Join
- Writing a Reduce-Side Join
Integrating Hadoop into the Enterprise Workflow
- Integrating Hadoop into an Existing Enterprise
- Loading Data from an RDBMS into HDFS by Using Sqoop
- Managing Real-Time Data Using Flume
- Accessing HDFS from Legacy Systems with FuseDFS and HttpFS
An Introduction to Hive, Imapala, and Pig
- The Motivation for Hive, Impala, and Pig
- Hive Overview
- Impala Overview
- Pig Overview
- Choosing Between Hive, Impala, and Pig
An Introduction to Oozie
- Introduction to Oozie
- Creating Oozie Workflows
