Cloudera Developer for Apache Hadoop Training Course

Course Summary

This four-day training course is for developers who want to learn to program and use Apache Hadoop to build powerful data processing applications.

Duration

4 days

Objectives

  • How MapReduce and the Hadoop Distributed File System work
  • How to write MapReduce code in Java or other programming languages
  • What issues to consider when developing MapReduce jobs
  • How to implement common algorithms in Hadoop
  • Best practices for Hadoop development and debugging
  • How to leverage other project such as Apache Hive, Apache Pig, Sqoop and Oozie
  • Advanced Hadoop API topics required for real-world data analysis

Prerequisites

This course is designed for developers with some programming knowhow (preferably Java). Existing knowledge of Hadoop is not required.

Additional Notes

Download the full agenda for Cloudera's Developer Training for Apache Hadoop.

Throughout the course, students write Hadoop code and perform other Hands-On Exercises to solidify their understanding of the concepts being presented.

Upon completion of the course, attendees receive a Cloudera Certified Developer for Apache Hadoop (CCDH) practice test.

Certification is a great differentiator; it helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.

Outline

Introduction

The Motivation For Hadoop

  • Problems with traditional large-scale systems
  • Requirements for a new approach

Hadoop: Basic Concepts

  • An Overview of Hadoop
  • The Hadoop Distributed File System
  • Hands-On Exercise
  • How MapReduce Works
  • Hands-On Exercise
  • Anatomy of a Hadoop Cluster
  • Other Hadoop Ecosystem Components

Writing a MapReduce Program

  • The MapReduce Flow
  • Examining a Sample MapReduce Program
  • Basic MapReduce API Concepts
  • The Driver Code
  • The Mapper
  • The Reducer
  • Hadoop’s Streaming API
  • Using Eclipse for Rapid Development
  • Hands-on exercise
  • The New MapReduce API

Integrating Hadoop Into The Workflow

  • Relational Database Management Systems
  • Storage Systems
  • Importing Data from RDBMSs With Sqoop
  • Hands-on exercise
  • Importing Real-Time Data with Flume
  • Accessing HDFS Using FuseDFS and Hoop

Delving Deeper Into The Hadoop API

  • More about ToolRunner
  • Testing with MRUnit
  • Reducing Intermediate Data With Combiners
  • The configure and close methods for Map/Reduce Setup and Teardown
  • Writing Partitioners for Better Load Balancing
  • Hands-On Exercise
  • Directly Accessing HDFS
  • Using the Distributed Cache
  • Hands-On Exercise

Common MapReduce Algorithms

  • Sorting and Searching
  • Indexing
  • Machine Learning With Mahout
  • Term Frequency – Inverse Document Frequency
  • Word Co-Occurrence
  • Hands-On Exercise

Using Hive and Pig

  • Hive Basics
  • Pig Basics
  • Hands-on exercise

Practical Development Tips and Techniques

  • Debugging MapReduce Code
  • Using LocalJobRunner Mode For Easier Debugging
  • Retrieving Job Information with Counters
  • Logging
  • Splittable File Formats
  • Determining the Optimal Number of Reducers
  • Map-Only MapReduce Jobs
  • Hands-On Exercise

More Advanced MapReduce Programming

  • Custom Writables and WritableComparables
  • Saving Binary Data using SequenceFiles and Avro Files
  • Creating InputFormats and OutputFormats
  • Hands-On Exercise

Joining Data Sets in MapReduce

  • Map-Side Joins
  • The Secondary Sort
  • Reduce-Side Joins

Graph Manipulation in Hadoop

  • Introduction to graph techniques
  • Representing graphs in Hadoop
  • Implementing a sample algorithm: Single Source Shortest Path

Creating Workflows With Oozie

  • The Motivation for Oozie
  • Oozie’s Workflow Definition Format
  • Hands-On Exercise

Upcoming Classes

Australia

Location Apr 2014 May 2014 Jun 2014 Jul 2014 Aug 2014
Melbourne May 27 – May 30

Classes in bold are guaranteed to run!