Hadoop

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Hadoop is an Apache project (i.e. open source software) to store & process Big Data. Hadoop stores Big Data in a distributed & fault tolerant manner over commodity hardware. Afterwards, Hadoop tools are used to perform parallel data processing over HDFS (Hadoop Distributed File System).

As organisations have realized the benefits of Big Data Analytics, so there is a huge demand for Big Data &Hadoop professionals. Companies are looking for Big data & Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop& Flume.

  • Introduction to Big Data and Hadoop
  • Lesson 1 - Introduction to Big Data and Hadoop
    • Introduction to Big Data and Hadoop
    • Objectives
    • Need for Big Data

    • Three Characteristics of Big Data
    • Characteristics of Big Data Technology
    • Appeal of Big Data Technology
    • Handling Limitations of Big Data
    • Introduction to Hadoop
    • Hadoop Configuration
    • Apache Hadoop Core Components
    • Hadoop Core Components—HDFS
    • Hadoop Core Components—MapReduce
    • HDFS Architecture
    • Ubuntu Server—Introduction
    • Hadoop Installation—Prerequisites
    • Hadoop Multi-Node Installation—Prerequisites
    • Single-Node Cluster vs. Multi-Node Cluster
    • MapReduce
    • Characteristics of MapReduce
    • Real-Time Uses of MapReduce
    • Prerequisites for Hadoop Installation in Ubuntu Desktop 12.04
    • Hadoop MapReduce—Features
    • Hadoop MapReduce—Processes
    • Advanced HDFS–Introduction
    • Advanced MapReduce
    • Data Types in Hadoop
    • Distributed Cache

    • Distributed Cache (contd.)

    • Joins in MapReduce

    • Introduction to Pig

    • Components of Pig

    • Data Model

    • Pig vs. SQL

    • Prerequisites to Set the Environment for Pig Latin

    • Summary

  • Lesson 2 - Hive HBase and Hadoop Ecosystem Components

     
    • Hive, HBase and Hadoop Ecosystem Components
    • Objectives
    • Hive—Introduction
    • Hive—Characteristics
    • System Architecture and Components of Hive
    • Basics of Hive Query Language
    • Data Model—Tables
    • Data Types in Hive
    • Serialization and De serialization
    • UDF/UDAF vs. MapReduce Scripts
    • HBase—Introduction
    • Characteristics of HBase
    • HBase Architecture
    • HBase vs. RDBMS
    • Cloudera—Introduction
    • Cloudera Distribution
    • Cloudera Manager
    • Hortonworks Data Platform
    • MapR Data Platform
    • Pivotal HD
    • Introduction to ZooKeeper
    • Features of ZooKeeper
    • Goals of ZooKeeper
    • Uses of ZooKeeper
    • Sqoop—Reasons to Use It
    • Sqoop—Reasons to Use It (contd.)
    • Benefits of Sqoop
    • Apache Hadoop Ecosystem
    • Apache Oozie
    • Introduction to Mahout

    • Usage of Mahout

    • Apache Cassandra

    • Apache Spark

    • Apache Ambari

    • Key Features of Apache Ambari

    • Hadoop Security—Kerberos

    • Summary

  • Once you complete this master’s program, you will receive the course completion certificate by DICS PITAMPURA

DICS PITAMPURA Course Completion Certificate will be awarded upon the completion of the project work (after the expert review) and upon scoring at least 50% marks in the quiz. DICS PITAMPURA certification is well recognized in top MNCs .

The market for Big Data analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Hiring managers are looking for certified Big Data Hadoop professionals. Our Big Data &Hadoop Certification Training helps you to grab this opportunity and accelerate your career. Our Big Data Hadoop Course can be pursued by professional as well as freshers. It is best suited for:

  • Software Developers, Project Managers
  • Software Architects
  • ETL and Data Warehousing Professionals
  • Data Engineers
  • Data Analysts & Business Intelligence Professionals
  • DBAs and DB professionals
  • Senior IT Professionals
  • Testing professionals
  • Mainframe professionals
  • Graduates looking to build a career in Big Data Field

For pursuing a career in Data Science, knowledge of Big Data, Apache Hadoop & Hadoop tools are necessary.

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