Objectives

Through instructor-led discussion and interactive, hands-on exercises, 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

Public

This course is best suited to developers and engineers

Prerequisites

Programming experience. Knowledge of Java is strongly recommended and is required to complete the exercises.

Post-Training

Methods

Program

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 OutputFormatsCommon 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

Environment

Virtual Machine with Cloudera

Tags

Hadoop, MapReduce