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Big Data And Hadoop at ScholarsPro

#703, 30th Main, 1st Phase, 2nd Stage,
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About The Course:-

Apache Hadoop is a new technology that allows large data volumes to be organized and processed while keeping the data on the original data storage cluster. It is an open source project for processing large datasets in parallel with the use of low level commodity machines. Hadoop is built on two main parts; a special file system called Hadoop Distributed File System (HDFS) and the MapReduce Framework.

Who Should Take this?

This course is designed for individuals seeking to gain expertise in the concepts, tools and the platform of Hadoop needed for processing large data sets. Further, this course is designed to meet the need to fulfil professional development training requirements on Hadoop to process large data sets (3 Continuing Education Units conferred upon successful completion of the course exit examination).

This course requires no prior knowledge of Java, Hadoop Cluster Administration or Apache Hadoop. Fundamental knowledge of Linux basics is necessary as Hadoop runs on Linux.

Course Objectives:-

The purpose of the three (6) weeks intensive certificate program is to provide high level understanding and the capabilities of Hadoop (MapReduce, HDFS), Hive, HBase, Pig, Sqoop, Scala & Spark for processing large volume of data workloads with various industry use cases.

Hadoop concepts and evolution

Brief background of Hadoop
Apache Hadoop and evolution
Data storage and Analysis
Comparison with (Relational Data Base Management System) RDBMS
Basic Hadoop MapReduce Features

Side data distribution
MapReduce Library Classes

Hadoop MapReduce types and formats

MapReduce Types
Input formats
Output formats

How MapReduce works?

Anatomy of a Map Reduce Job RUN
Job Scheduling
Shuffle and Sort
Task execution

Basic understandings of Hadoop Distributed File Systems (HDFS)

HDFS concepts
The design of HDFS
Hadoop File systems
The command line interface
The java interface
Data Flow

Hadoop I/O operations

Data Integrity
File based data structure

Setting up a Hadoop Cluster

Cluster specification
Cluster setup and Installation
Hadoop Configuration
Hadoop in the cloud

Administering Hadoop environment

Persistent data structure

Introduction to Hive

Basic features of Hive
Comparison with traditional databases

Basics of Hbase

Basics of Hbase
Comparison with traditional databases

Basics of Pig
Comparison with Hive as well as with other programming languages

Basics of Sqoop
Understanding purposes and utilisation

Basics of Scala
Comparison with Java

Basics of Spark and programming in Spark
Understand the different ways Spark uses Hadoop

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Start-End Dates: Contact Institute
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
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