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Hadoop Training Centre In Indore

Hadoop is an open source, a Java-based programming framework that supports the process and storage of extremely large data sets in a very distributed computing atmosphere. it's a part of the Apache project sponsored by the Apache software Foundation.

Hadoop makes it possible to run applications on systems with thousands of commodity hardware nodes, and to handle thousands of terabytes of data. Its distributed file system facilitates rapid information transfer rates among nodes and permits the system to continue operating just in case of a node failure. This approach lowers the chance of catastrophic system failure and surprising information loss, although a major number of nodes become inoperative. Consequently, Hadoop quickly emerged as a foundation for big data processing tasks, like scientific analytics, business and sales designing, and process enormous volumes of detector information, as well as from net of things sensors.

Why Hadoop

Hadoop is changing the perception of handling Big Data especially the unstructured data. Let’s know how Apache Hadoop software library, which is a framework, plays a vital role in handling Big Data. Apache Hadoop enables surplus data to be streamlined for any distributed processing system across clusters of computers using simple programming models. It truly is made to scale up from single servers to a large number of machines, each and every offering local computation, and storage space. Instead of depending on hardware to provide high-availability, the library itself is built to detect and handle breakdowns at the application layer, so providing an extremely available service along with a cluster of computers, as both versions might be vulnerable to failures.

Hadoop Course Content

INTRODUCTION

Big Data
3Vs
Role of Hadoop in Big data
Hadoop and its ecosystem
Overview of other Big Data Systems
Requirements in Hadoop
UseCases of Hadoop

HDFS

Design
Architecture
Data Flow
CLI Commands
Java API
Data Flow Archives
Data Integrity
WebHDFS
Compression

MAPREDUCE

Theory
Data Flow (Map – Shuffle – Reduce)
Programming [Mapper, Reducer, Combiner, Partitioner]
Writables
InputFormat
Outputformat
Streaming API

ADVANCED MAPREDUCE PROGRAMMING

SCounters
SCustomInputFormat
SDistributed Cache
SSide Data Distribution
SJoins
SSorting
SToolRunner
SDebugging
SPerformance Fine tuning

ADMINISTRATION – Information required at Developer level

Hardware Considerations – Tips and Tricks
Schedulers
Balancers
NameNode Failure and Recovery

HBase

NoSQL vs SQL
CAP Theorem
Architecture
Configuration
Role of Zookeeper
Java Based APIs
MapReduce Integration
Performance Tuning

HIVE

Architecture
Tables
DDL – DML – UDF – UDAF
Partitioning
Bucketing
Hive-Hbase Integration
Hive Web Interface
Hive Server

String Handling

Overview of String in C
Reading String from Terminal
Writing String to console screen
String Handling Functions - string.h
gets() & puts() functions

OTHER HADOOP ECOSYSTEMS

Pig (Pig Latin , Programming)
Sqoop (Need – Architecture ,Examples)
Introduction to Components (Flume, Oozie,ambari)

Benefits of Hadoop Training

Complete code explanation and implementation
Course Starts from installation of technology to deployment of product
Trainers from Industry with good hand on experience
You can develop your own programs after understanding the basics with our experienced Faculties
Weekdays, fast track and weekend Batches
Certificate after Successful completion of Training
Online and Offline material support for better learning
Software and Installation support will be provided
Regular Machine Test for better understandings
Free Live Project Support to all participants
Industry Exposure via Live Troubleshooting
Guaranteed placement to meritorious students

Required Software/ Platforms for hadoop Training

Any OS
Java Oracle JDK 1.6
Server: GlassFish, Tomcat

Hadoop Training