Hadoop Development Online Training/support 3092003848

[Davidjhon]

We Provide Online Training|Training|Support on Hadoop Development  with Real Time Experts

Cours Outline:

 

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

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 Framework.s

Writing Unit Tests with MRUnit

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

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

An Introduction to Hive, Imapala, and Pig

The Motivation for Hive, Impala, and Pig.

 

You can reach us @   3092003878 or Email us: [email protected]

 

[Butterthief]

1 minute ago, Davidjhon said:

We Provide Online Training|Training|Support on Hadoop Development  with Real Time Experts

Cours Outline:

 

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

 

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 Framework.s

 

Writing Unit Tests with MRUnit

 

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

 

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

 

An Introduction to Hive, Imapala, and Pig

 

The Motivation for Hive, Impala, and Pig.

 

 

 

You can reach us @   3092003878 or Email us: [email protected]

 

 

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[Buttertheif]

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