Hadoop for Developers (2 days) Schulung

Haupt-Reiter

Schulungsprache

Dieser Kurs wird auf Deutsch und Englisch angeboten

Course Code

Dauer

14 Stunden (usually 2 days including breaks)

Schulungsübersicht

Introduction

  • What is Hadoop?
  • What does it do?
  • How does it do it?

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 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
  • 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

Public Classroom Public Classroom
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Private Classroom Private Classroom
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Selbststudium Selbststudium
Das Selbststudium ermöglicht es Ihnen von Zuhause aus zu lernen. Vielfältige Materialien, wie Videos, Texte und Selbsttests stehen Ihnen dafür zur Verfügung.
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The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.

Number of Delegates Public Classroom Private Classroom Private Remote
1 5030EUR 5030EUR 4530EUR
2 3075EUR 3050EUR 2800EUR
3 2423EUR 2390EUR 2223EUR
4 2098EUR 2060EUR 1935EUR
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