Become a Hadoop Expert by mastering MapReduce, Yarn, Pig, Hive, HBase, Oozie, Flume and Sqoop, while working on industry based Use-cases and Projects.
Preview
By the end of this hadoop certication course, you will learn to:
Master the concepts of HDFS and MapReduce framework
Understand Hadoop 2.x Architecture
Setup Hadoop Cluster and write Complex MapReduce programs
Learn data loading techniques using Sqoop and Flume
Perform data analytics using Pig, Hive and YARN
Implement HBase and MapReduce integration
Implement Advanced Usage and Indexing
Schedule jobs using Oozie
Implement best practices for Hadoop development
Work on a real life Project on Big Data Analytics
Understand Spark and its Ecosystem
Learn how to work in RDD in Spark
Course Contents
Day 1
Introduction
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
Day 2
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
Day 3
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
Day 4
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 Writable Comparable 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
Day 5
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, Impala, 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
Apps2Fusion Articles 