Authorised Cloudera Developer Training for Apache Hadoop | 4 Days

Xebia IT Architects India Private Limited
In Bangalore

Rs 74,400
VAT incl.
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Important information

  • Training
  • Beginner
  • Bangalore
  • Duration:
    4 Days
Description

Xebia is the authorised Cloudera training partner. We are running more than 3 batches per month of Cloudera with certifictions.

Cloudera Developer Training for Apache Hadoop

Important information
Venues

Where and when

Starts Location
On request
Bangalore
Karnataka, India
See map

Frequent Asked Questions

· What are the objectives of this course?

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as: Using the Spark shell for interactive data analysis The features of Spark’s Resilient Distributed Datasets How Spark runs on a cluster Parallel programming with Spark Writing Spark applications Processing streaming data with Spark

· Who is it intended for?

This course is best suited to developers and engineers who have programming experience.

· Requirements

Knowledge of Java is strongly recommended and is required to complete the hands-on exercises.

What you'll learn on the course

Java
Hadoop
Cloudera Developer
Apache

Teachers and trainers (1)

Xebia Xebia
Xebia Xebia
Trainer

Course programme

Course Outline:
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
  • Mappersn
  • 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 Output Formats

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

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