Cloudera Developer Training for MapReduce

Course Overview

Cloudera University’s four-day developer training course delivers the key concepts and expertise participants need to create robust data processing applications using Apache Hadoop. From workflow implementation and working with APIs through writing MapReduce code and executing joins, Cloudera’s training course is the best preparation for the real-world challenges faced by Hadoop developers.

Who Should Attend

This course is best suited to developers and engineers who have programming experience. Knowledge of Java is strongly recommended and is required to complete the hands-on exercises.

Course Objectives

Skills gained in this training include:

  • The internals of MapReduce and HDFS and how to write MapReduce code
  • 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
  • Advanced Hadoop API topics required for real-world data analysis

    Course Outline

    1 - Introduction

    2 - The Motivation for Hadoop

    • Problems with Traditional Large-Scale Systems
    • Introducing Hadoop
    • Hadoopable Problems

    3 - Hadoop: Basic Concepts and HDFS

    • The Hadoop Project and Hadoop Components
    • The Hadoop Distributed File System

    4 - Introduction to MapReduce

    • MapReduce Overview
    • Example: WordCount
    • Mappers
    • Reducers

    5 - Hadoop Clusters and the Hadoop Ecosystem

    • Hadoop Cluster Overview
    • Hadoop Jobs and Tasks
    • Other Hadoop Ecosystem Components

    6 - 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

    7 - Writing a MapReduce Program Using Streaming

    • Writing Mappers and Reducers with the Streaming API

    8 - Unit Testing MapReduce Programs

    • Unit Testing
    • The JUnit and MRUnit Testing Frameworks
    • Writing Unit Tests with MRUnit
    • Running Unit Tests

    9 - 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

    10 - 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

    11 - Partitioners and Reducers

    • How Partitioners and Reducers Work Together
    • Determining the Optimal Number of Reducers for a Job
    • Writing Customer Partitioners

    12 - 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

    13 - Common MapReduce Algorithms

    • Sorting and Searching Large Data Sets
    • Indexing Data
    • Computing Term Frequency — Inverse Document Frequency
    • Calculating Word Co-Occurrence
    • Performing Secondary Sort

    14 - Joining Data Sets in MapReduce Jobs

    • Writing a Map-Side Join
    • Writing a Reduce-Side Join

    15 - 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

    16 - 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

    17 - An Introduction to Oozie

    • Introduction to Oozie
    • Creating Oozie Workflows

  • Enroll Today

    This is a 4-day class

    Price: $2,995.00
    Payment Options

    ILT Instructor‑Led Training

    OLL Online LIVE

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    Class times are listed Eastern time. This class is available for Private Group Training

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