Big Data Hadoop Training in Pune, India

Live Online & Classroom Enterprise Training

SpringPeople offers the best Apache Hadoop & Big Data training In Pune. This course will equip candidates to lead and develop Hadoop applications necessary for Big Data analysis. With this training, candidates will also gain an in-depth knowledge of Hadoop’s core concepts such as Yarn, Pig, Hive, Impala, MapReduce. Sqoop, HBase, Flume, and Oozie. Led by industry experts, this training will expose candidates to real-life big data projects on the cloud which will help them to become an industry ready Hadoop expert. This course will also involve various cases studies that will show candidates how organizations implement & deploy Hadoop clusters.

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What is Hadoop Training about?

Gain comprehensive working knowledge of the important Hadoop tools required to become a top Big Data Developer with our Big Data course. Learn from industry experts how various organizations implement and deploy Hadoop clusters with detailed case studies. You can work on real life big data projects on the cloud to be an industry ready Hadoop expert.

This course is recommended as the foundation course for all professionals looking to develop Hadoop big data applications for their organizations.

What are the objectives of Hadoop Training ?

At the end of this Big Data course, you will be able to:

Internalize vital big data concepts
Demonstrate and implement Hive, Hbase, Flume, Sqoop, Oozie and Pig
Work on Hadoop Distributed File System (HDFS)
Handle Hadoop Deployment
Gain expertise on Hadoop Administration and Maintenance
Master Map-Reduce techniques
Develop Hadoop 2.7 applications using Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop and Flume

Who is Hadoop Training for?

Anyone who wants to develop big data applications using Hadoop
Teams getting started or working on Hadoop based projects

What are the prerequisites for Hadoop Training?

Basic programming knowledge is recommended

Available Training Modes

Live Online Training

18 Hours

Classroom Training

3 Days

Course Outline Expand All

Expand All

1. Introduction to BigData

Which data is called as BigData

What are business use cases for BigData

BigData requirement for traditional Data warehousing and BI space

BigData solutions

2. Introduction to Hadoop

The amount of data processing in today's life

What Hadoop is why it is important

Hadoop comparison with traditional systems

Hadoop history

Hadoop main components and architecture

3. Hadoop Distributed File System (HDFS)

HDFS overview and design

HDFS architecture

HDFS file storage

Component failures and recoveries

Block placement

Balancing the Hadoop cluster

4. Hadoop Deployment

Different Hadoop deployment types

Hadoop distribution options

Hadoop competitors

Hadoop installation procedure

Distributed cluster architecture

Lab: Hadoop Installation

5. Working with HDFS

Ways of accessing data in HDFS

Common HDFS operations and commands

Different HDFS commands

Internals of a file read in HDFS

Data copying with 'distcp'

Lab: Working with HDFS

6. Hadoop Cluster Configuration

Hadoop configuration overview and important configuration file

Configuration parameters and values

HDFS parameters

MapReduce parameters

Hadoop environment setup

'Include' and 'Exclude' configuration files

Lab: MapReduce Performance Tuning

7. Hadoop Administration and Maintenance

Namenode/Datanode directory structures and files

Filesystem image and Edit log

The Checkpoint Procedure

Namenode failure and recovery procedure

Safe Mode

Metadata and Data backup

Potential problems and solutions / What to look for

Adding and removing nodes

Lab: MapReduce Filesystem Recovery

8. Job Scheduling

How to schedule Hadoop Jobs on the same cluster

Default Hadoop FIFO Schedule

Fair Scheduler and its configuration

9. Map-Reduce Abstraction

What MapReduce is and why it is popular

The Big Picture of the MapReduce

MapReduce process and terminology

MapReduce components failures and recoveries

Working with MapReduce

Lab: Working with MapReduce

10. Programming MapReduce Jobs

Java MapReduce implementation

Map() and Reduce() methods

Java MapReduce calling code

Lab: Programming Word Count

11. Input\/Output Formats and Conversion Between Different Formats

Default Input and Output formats

Sequence File structure

Sequence File Input and Output formats

Sequence File access via Java API and HDS

MapFile

Lab: Input Format

Lab: Format Conversion

12. MapReduce Features

Joining Data Sets in MapReduce Jobs

How to write a Map-Side Join

How to write a Reduce-Side Join

MapReduce Counters

Built-in and user-defined counters

Retrieving MapReduce counters

Lab: Map-Side Join

Lab: Reduce-Side Join

13. Introduction to Hive, Hbase, Flume, Sqoop, Oozie and Pig

Hive as a data warehouse infrastructure

Hbase as the Hadoop Database

Using Pig as a scripting language for Hadoop

14. Hadoop Case studies

How different organizations use Hadoop cluster in their infrastructure

Who is the instructor for this training?

The trainer for this Big Data Hadoop Training in Pune, India has extensive experience in this domain, including years of experience training & mentoring professionals.

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