Big Data Analytics with Hadoop-Level 1

Big data refers to the use of advanced data analytics methods that help extract value from large data sets both structured an...


Content Provider

40 hrs



Mode Of Delivery

Valid for 6 months post activation

Course Validity




Certification By

This is a paid course.

Course Fee

  • 1,800/-/- 599/-/-


Big data refers to the use of advanced data analytics methods that help extract value from large data sets both structured and unstructured. With the availability of large data sets, there is a need for tools to computationally analyse and help reveal patterns, trends, associations to make meaningful decisions.


Module 1 - Introduction to Big Data

Rise of Big Data, Hadoop vs traditional systems, Hadoop Master-Slave architecture, HDFS Architecture, NameNode, DataNode, Secondary Node, JobTracker, TaskTracker.

Module 2 – HDFS and MapReduce architecture

Core components of Hadoop, Anatomy of Read and Write data on HDFS, MapReduce architecture Flow, JobTracker and TaskTracker.

Module 3 -  Hadoop Configuration

Hadoop modes, Hadoop terminal commands, Cluster configuration, Web ports, Hadoop configuration files, Reporting, Recovery, MapReduce in action.

Module 4 - Understanding Hadoop MapReduce framework

Overview of the MapReduce framework, Use cases of MapReduce, Anatomy of MapReduce program, Mapper/Reducer class, Driver code and Combiner and Partitioner.

Module 5: Advance MapReduce - Part 1

Write your own Partitioner, Writing Map and Reduce, Map side/Reduce side Join, Distributed Join, Distributed cache, Counters and joining Multiple datasets in MapReduce.

Module 6: Advance MapReduce - Part 2

MapReduce internals, Input format, Custom input format, Writable and Comparable, Output format, files, JUnit and MRUnit testing frameworks.

Module 7: Apache Pig

PIG vs MapReduce, PIG architecture & Data types, PIG Latin relational operators, PIG Latin Join and CoGroup, PIG Latin group and union, Describe, Explain, Illustrate, PIG Latin: File loaders & UDF. 

Learning Outcomes

  • Understand the concept of BigData
  • Understand the concept of Hadoop
  • Understand the internals of MapReduce and YARN
  • Understand the different modes and distribution of Hadoop
  • Write MapReduce job for word count
  • Create one node Hadoop cluster

Who Should Attend?

  • Engineering and IT students
  • Graduates with a programming background

 Job Prospects

  • Data Analyst
  • Java Developer
  • Hadoop Developer
  • Business Analyst
  • Software Developer
  • SAS Analyst


After completing this course and successfully passing the certification examination, the student will be awarded the “Big Data Analytics with Hadoop1” certification.

If a learner chooses not to take up the examination, they will still get a 'Participation Certificate'