Introduction to Big data Analytics: Introduction, Need of Big Data, Big Data, Classification of Data-Structured, Semi-structured and Unstructured, Big data Definitions, Big data Characteristics, Big data Types, Big data Classification , Big Data handling Techniques, Scalability and Parallel Processing.

Introduction to Hadoop- Hadoop and its echo System, Hadoop core Components, Features of Hadoop, Hadoop Ecosystem Components, Hadoop Streaming, Hadoop Pipes,Hadoop Distributed file System, HDFS Data Storage, HDFS Commands, Mapreduced Framework and Programming Model, Hadoop Mapreduced Framework, MapReduce Programming Model, Hadoop Ecosystem Model, Hadoop Ecosystem

NoSQL Big Data management, MongoDB and Cassandra: Introduction, NoSQL Data Store, NoSQL, Schema-less Models, Increasing Flexibility for Data Manipulation, NoSQL Data Architecture Patterns, NoSQL to Manage Big Data, Using NoSQL to Manage Big Data, Shared-Nothing Architecture for Big Data Tasks, Choosing the Distribution Models, ways of Handling Big Data Problems, MongoDB database, Cassandra Database.

MapReduce, hive and Pig:Introduction, MapReduced Map Tasks and MapReduced Execution, Composing MapReduced for Calculation and Algorithms, Hive, HiveQL, Pig, Apache Pig- Grunt Shell, Installing Pig, Pig Latin Data Model, Pig Latin and Developing Pig Latin Scripts.

Spark and Big Data Analysis, Introduction, Spark, Introduction to Big Data Tool-Spark, Introduction to Data Analysis with Spark, Downloading Spark, and Programming using RDDs and MLIB, Downloading, installing Spark and getting Strated, Programming with RDDs, Machine Learning with MLib, Data ETL (Extarct, Transform and Load )Process, Composing Spark Program steps for ETL, Introduction to Analytics, Reporting and Visualizing.