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Hadoop Developer at Vepsun Institute

#100 & 104, SR Aracade, Thualsi Theater Road, ,
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Lesson objectives

Hands-on instruction and practice installing and configuring Hadoop & Big Data

Evaluation measures

Assessment by trainer of student progress as outlined in session plan. Evaluation in LMS.

Equipment Required

Physical Machine

Material & resources

Training Plan

Pre-course / pre-requisites

  • Basic Knowlwdge of Java
  • Basic Knowlwdge of SQL
  • Basic Knowlwdge of UNIX
  • Knowdge of Windows.

Learning Objectives: At the end of Hadoop Developer Training course, participants will be able to:

  • Completely understand Apache Hadoop Framework.
  • Learn to work with HDFS.
  • Discover how MapReduce works with data and processes it.
  • Design and develop big data applications using Hadoop Ecosystem.
  • Learn how YARN helps in managing resources into clusters.
  • Write as well as execute programs in YARN.
  • Implement MapReduce Integration, HBase, Advanced Indexing and Advanced Usage.
  • Work on assignments.

Module 1: Introduction of Big Data & Hadoop

Module 2: Big Data Overview

Topics covered on Overview
  • Why Hadoop is needed
  • How Hadoop originated
  • What problems Hadoop solves
  • How Hadoop compares to other large-scale systems

Module 3: The Motivation for Hadoop

Topics covered on Motivation for Hadoop
  • Problems with Traditional Large-Scale Systems
  • Introducing Hadoop
  • Hadoopable Problems

Module 4: Hadoop: Basic Concepts and HDFS

Topics covered on Basic Concepts and HDFS
  • The Hadoop Project and Hadoop Components
  • The Hadoop Distributed File System

Module 5: Introduction to Map Reduce

Topics covered on MapReduce
  • Map Reduce Overview
  • Example: Word Count
  • Mappers
  • Reducers

Module 6: Hadoop Clusters and the Hadoop Ecosystem

Topics covered on Clusters and Ecosystem
  • Hadoop Cluster Overview
  • Hadoop Jobs and Tasks
  • Other Hadoop Ecosystem Components

Module 7: Writing a Map Reduce Program in Java & Pig/Hive

Topics covered on Writing a Map Reduce Program
  • Basic Map Reduce API Concepts
  • Writing Map Reduce Drivers, Mappers, and Reducers in Java
  • Speeding Up Hadoop Development by Using Eclipse
  • Differences Between the Old and New MapReduce APIs

Module 8: Data Input and Output

Topics covered on Data Input and Output
  • What issues to consider when planning your Hadoop cluster
  • What types of hardware are typically used for Hadoop nodes
  • How to optimally configure your network topology
  • How to select the right operating system and Hadoop distribution

Module 9: Delving Deeper into the Hadoop API

Topics covered on Hadoop API
  • Using the Tool Runner Class
  • Setting Up and Tearing Down Mappers and Reducers
  • Decreasing the Amount of Intermediate Data with Combiners
  • Accessing HDFS Programmatically
  • Using The Distributed Cache

Module 10: Common Map Reduce Algorithms

Topics covered on Common Map Reduce Algorithms
  • Sorting and Searching Large Data Sets
  • Indexing Data
  • Computing Term Frequency — Inverse Document Frequency
  • Calculating Word Co-Occurrence
  • Performing Secondary Sort

Module 11: An Introduction to Hive, Imapala, and Pig

Topics covered on Hive, Imapala, and Pig
  • The Motivation for Hive, Impala, and Pig
  • Hive Overview
  • Impala Overview
  • Pig Overview
  • Choosing Between Hive, Impala, and Pig

Module 12: An Introduction to Oozie

Topics covered on Oozie
  • Introduction to Oozie
  • Creating Oozie Workflows

Module 13: Hadoop Interview Discussion

Topics covered on Hadoop Interview Discussion
  • What is required to crack Hadoop interview
  • How we need to prepare
  • All interview aspect
  • Command Problems during interview
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hadoop vepsun bangalore training course
Start-End Dates: 15 Oct 16 - 14 Nov 16
Course Duration: 30 days
Instructional Level: Appropriate for All
Live Projects
Doubt Clearing Sessions
Reading Material
EMI Option
Online Support
Post completion course access
Practice Exams
Placement assistance
Refund Policy
Post completion support

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