Hadoop Developer Training

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Hadoop Developer Training

About SimplyAnalytics

We are the best Training institute for learning Hadoop Developer Training in Chennai . We have expert trainers and excellent materials to transform your skills to fit into the job market .

About The Course

  • Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of core concepts will be covered in the course along with implementation on varied industry use-cases.

Course Objectives

At the end of the course, participants should be able to:

  • 1. Master the concepts of HDFS and MapReduce framework.
  • 2. Understand Hadoop 2.x Architecture.
  • 3. Setup Hadoop Cluster and write Complex MapReduce programs.
  • 4. Learn the data loading techniques using Sqoop and Flume.
  • 5. Perform Data Analytics using Pig, Hive and YARN.
  • 6. Implement HBase and MapReduce Integration.
  • 7. Implement Advanced Usage and Indexing.
  • 8. Schedule jobs using Oozie.
  • 9. Implement best Practices for Hadoop Development.
  • 10. Work on a Real Life Project on Big Data Analytics.

 Module 1 : Introduction

  • 1.Introduction to Big Data and Hadoop
  • 2.What is Big Data?
  • 3.Types of Data
  • 4.Need for Big Data
  • 5.Characteristics of Big Data
  • 6.Traditional IT Analytics Approach
  • 7.Big Data—Use Cases
  • 8.Handling Limitations of Big Data
  • 9.Introduction to Hadoop
  • 10.History and Milestones of Hadoop

Module 2 : Getting Started With Hadoop

  • 1.VMware Player—Introduction
  • 2.Installing VMware Player
  • 3.Setting up the Virtual Environment
  • 4.Oracle VirtualBox to Open a VM

Module 3 : Hadoop Architecture

  • 1.Hadoop Cluster in commodity hardware
  • 2.Hadoop core services and components
  • 3.Regular file system vs. Hadoop
  • 4.HDFS layer e. HDFS operation principle

Module 4 : Hadoop Deployment

  • 1.Introduction to Ubuntu Server
  • 2.Hadoop installation
  • 3.Single node and multi node configuration
  • 4.Hadoop Configuration in cluster environment
  • 5.Installing Hadoop

Module 5 :  MapReduce

  • 1.Introdution to MapReduce
  • 2.Hadoop MapReduce example
  • 3.Hadoop MapReduce Characteristics
  • 4.Setting up your MapReduce Environment
  • 5.Building a MapReduce Program
  • 6.MapReduce Requirements and Features
  • 7.MapReduce Java Programming in Eclipse
  • 8.Checking Hadoop Environment for MapReduce
  • 9.MapReduce

Module 6 : Advanced HDFS and MapReduce

  • 1.HDFS Benchmarking
  • 2.Setting up HDFS Blocks
  • 3.Decommissioning a DataNode
  • 4.Advanced MapReduce
  • 5.Hadoop Data Types
  • 6.InputFormats in MapReduce
  • 7.OutputFormats in MapReduce
  • 8.Distributed Cache
  • 9.Joins in MapReduce

Module 7 :  PIG

  • 1.Getting started with Apache PIG
  • 2.Running PIG in Different Modes
  • 3.PIG Architecture
  • 4.PIG Latin Statements
  • 5.PIG Model and Operators
  • 6.Arithmetic and Boolean Operators
  • 7.Cast and Comparison Operators
  • 8.Relational Operators
  • 9.PIG Streaming
  • 10.Eval Functions
  • 11.Load and Store Functions
  • 12.Tuple and Bag Functions
  • 13.PIG Scripts and UDF’s
  • 14.Create and Run PIG Scripts
  • 15.Writing JAVA UDF’s
  • 16.Control Structures
  • 17.Embedded PIG in JAVA
  • 18.PIG Macros
  • 19.Parameter Substitution
  • 20.Shell and Utility Commands
  • 21.Shell Commands
  • 22.Utility Commands
  • 23.Compression with PIG
  • 24.Compressed Files
  • 25.Compress the Results of Intermediate Jobs
  • 26.Testing and Diagnostics
  • 27.Diagnostic Operators
  • 28.PIGUnit Testing
  • 29.Using HCatalog With Pig
  • 30.Advanced Pig
  • 31.Parameters In Pig Scripts
  • 32.Pig & Oozie
  • 33.Embedded Pig
  • 34.Pig UDF’s & Streaming
  • 35.User Defined Functions
  • 36.Streaming Pig Data Through Custom Scripts

Module 8 : HIVE

  • 1.What is Hive
  • 2.Advantages of hive
  • 3.Hive Architecture
  • 4.Hive Data Types
  • 5.Hive QL
  • 6.DDL on Databases
  • 7.DDL on Tables
  • 8.Different Tables in Hive
  • 9.Advanced DDL on tables
  • 10.File Formats in Hive
  • 11.DML – Loading Data in tables
  • 12.Managing Output
  • 13.Hive QL Queries
  • 14.Operators and Functions in hive
  • 15.Hive Clauses
  • 16.Joins in Hive
  • 17.Wordcount Example using Hive
  • 18.Hive View
  • 19.Hive Indexing
  • 20.Indexing with Additional Properties
  • 21.Tuning
  • 22.Executive Hive Queries in Different Modes
  • 23.Hadoop Tuning Parameters
  • 24.Compression with Hive
  • 25.Choosing a compression codec
  • 26.Sequence Files
  • 27.Sequence Files with Different Compression Types
  • 28.Running Compression with Hive Queries
  • 29.UDFs in Hive
  • 30.Functions in Hive
  • 31.Aggregation, Calling, table generating Functions
  • 32.Different Kinds of UDFs
  • 33.Creating and Calling UDFs in Hive
  • 34.Hive UDAFs
  • 35.Customizing Hive File and Record Formats
  • 36.TextFile and Sequence File Formats\
  • 37.RCFile Format
  • 38.CSV and JSON SerDe
  • 39.Hive Avro SerDe
  • 40.Hive Storage Handlers and NoSQL
  • 41.HBase
  • 42.HCatalog
  • 43.Need of Shared MetaStore
  • 44.What are HCatalog tables
  • 45.Partitioning Introduced
  • 46.The Rationale for Partitioning
  • 47.How Tables are Partitioned
  • 48.Using Partitioned Tables
  • 49.Dynamic Partitioning: Inserting data into partitioned tables
  • 50.Code-Along : Partitioning
  • Introducing Bucketing
  • 51.The Advantages of Bucketing
  • 52.How Tables are Bucketed
  • 53.Using Bucketed Tables
  • 54.Sampling
  • 55.Windowing
  • 56.Windowing – A Simple Example: Cumulative Sum
  • 57.Windowing – A More Involved Example: Partitioning
  • 58.Windowing – Special Aggregation Functions
  • 59.Join Optimizations in Hive
  • 60.Improving Join performance with tables of different sizes
  • 61.The Where clause in Joins
  • 62.The Left Semi Join
  • 63.Map Side Joins: The Inner Join
  • 64.Map Side Joins: The Left, Right and Full Outer Joins
  • 65.Map Side Joins: The Bucketed Map Join and the Sorted Merge Join

Module 9 : HBase

  • 1.HBase introduction
  • 2.Characteristics of HBase
  • 3.HBase Architecture
  • 4.Storage Model of HBase
  • 5.When to use HBase
  • 6.HBase Data Model
  • 7.HBase Families
  • 8.HBase Components
  • 9.Row Distribution between region servers Copyright © 2012-2014, Simplilearn, All rights reserved
  • 10.Data Storage
  • 11.Installation of HBase
  • 12.Configuration of HBase
  • 13.HBase Shell Commands

Module 10 : Commercial Distribution of Hadoop

  • 1.Cloudera
  • 2.Downloading Cloudera Quickstart VM
  • 3.Starting the Cloudera VM
  • 4.Exploring the Welcome Page
  • 5.Understanding Hue
  • 6.Understanding Cloudera Manager
  • 7.Hortonworks Data Platform
  • 8.MapR Data Platform
  • 9.Pivotal HD
  • 10.IBM InfoSphere BigInsights

Module 11 :  ZooKeeper Sqoop and Flume

  • 1.Introduction to ZooKeeper
  • 2.Features of ZooKeeper
  • 3.Challenges faced in distributed applications
  • 4.Coordination
  • 5.ZooKeeper: Goals and Uses
  • 6.ZooKeeper: Entities, Data Model, Services
  • 7.Client APIs
  • 8.Recipes of Zookeeper
  • 9.Introduction to Sqoop (Why, what, processing, under the hood)
  • 10.Importing data into Hive
  • 11.Importing data into HBase
  • 12.Exporting data from Hadoop using Sqoop
  • 13.Sqoop Connectors
  • 14.Introduction to Flume
  • 15.Flume Use Cases
  • 16.Configuring and Running Flume Agents

Module 12 : Ecosystem and its Components

  • 1.Hadoop Ecosystem
  • 2.Components Overview
  • 3.Overview of Apache Oozie
  • 4.Overview of Mahout
  • 5.Overview of Apache Cassandra
  • 6.Apache Spark

Module 13 : Hadoop Administration and Troubleshooting

  • 1.Commands Used in Hadoop Programming
  • 2.Different configurations of Hadoop cluster
  • 3.Port Numbers for Individual Hadoop Services Copyright © 2012-2014, Simplilearn, All rights reserved
  • 4.Performance monitoring
  • 5.Performance tuning
  • 6.Troubleshooting and Log observation
  • 7.Overview of Apache Ambari
  • 8.Hadoop Security Using Kerberos

Why choose SimplyAnalytics for Hadoop Developer Training in Chennai?

  • 1.100% Practical and placement oriented training.
  • 2.We are registered training organization.
  • 3.Expert trainers from IT industries.
  • 4.Placements Assistance.
  • 5.Flexible timings.
  • 6.Weekdays and weekend batches.
  • 7.Affordable fees.
  • 8.Air conditioned classroom.
  • 9.Wi-Fi enabled training institute.
  • 10. Best Lab specialities.

Are you located in any of these areas – Adambakkam, Camp Road, Chromepet, Ekkattuthangal, Guindy, kovilambakkam, Madipakkam, Medavakkam, Nanganallur, Navalur, Nungambakkam, OMR, Pallikaranai, Perungudi, Rajakilpakkam, Saidapet, Sholinganallur,Siruseri, St.Thomas Mount, T. Nagar, Tambaram, Tambaram East, Thiruvanmiyur, Thoraipakkam, Velachery, and West Mambalam.

Our Medavakkam office is just few kilometre away from your location. If you need the best Hadoop  Developer Training in Chennai travelling of extra kilometres is worth it .

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Course Features

  • Lectures 1
  • Quizzes 1
  • Duration 50 hours
  • Skill level All level
  • Language English
  • Students 0
  • Assessments Self
Curriculum is empty