About Us

Big Data & Hadoop

Big data refers to the large and complex set of data that are difficult to process using traditional processing systems. Stock exchanges like NYSE and BSE generate Terabytes of data every day. Social media sites like Facebook generates data that are approximately 500 times bigger than stock exchanges.

Hadoop is an open source project by Apache used for storage and processing of large volume of unstructured data in a distributed environment. Hadoop can scale up from a single server to thousands of servers.

Hadoop framework is used by large giants like Amazon, IBM, New York Times, Google, Facebook, Yahoo and the list is growing every day. Due to the larger investments companies make for Big Data the need for Hadoop Developers and Data Scientists who can analyze the data increases day by day.

Software professionals working on obsolete technologies, JAVA professionals, analytics professionals, ETL professionals, data warehouse professionals, test professionals, project managers can undergo Hadoop training in Chennai and make a career change. Our great data training in Chennai will provide you hands-on experience to meet the demands of the industry needs.

Upcoming Batches

09
Mar

Friday

7:00 AM IST

09
Apr

Monday

7:00 AM IST

16
Mar

Friday

7:00 AM IST

18
Apr

Wednesday

7:00 AM IST

Syllabus

  • Introduction
  • Overview to Big Data and Hadoop
  • Hadoop Ecosystem
  • Introduction
  • HDFS Architecture and Components
  • Block Replication Architecture
  • YARN Introduction
  • Introduction
  • Why Mapreduce
  • Small Data and Big Data
  • Data Types in Hadoop
  • Joins in MapReduce
  • What is Sqoop
  • Introduction
  • Interacting with Hive and Impala
  • Introduction
  • Data Types in Hive
  • Validation of Data
  • What is Hcatalog and Its Uses
  • Introduction
  • Types of File Format
  • Data Serialization
  • Importing MySql and Creating hivetb
  • Parquet With Sqoop
  • Introduction
  • Overview of the Hive Query Language
  • Introduction
  • Introduction to HBase
  • Introduction
  • Getting Datasets for Pig Development
  • Introduction
  • Spark - Architecture, Execution, and Related Concepts
  • RDD Operations
  • Functional Programming in Spark
  • Introduction
  • RDD Data Types and RDD Creation
  • Operations in RDDs
  • Introduction
  • Running Spark on YARN
  • Running a Spark Application
  • Dynamic Resource Allocation
  • Configuring Your Spark Application
  • Introduction
  • Parallel Operations on Partitions
  • Running a Spark Application
  • Dynamic Resource Allocation
  • Configuring Your Spark Application
  • Introduction
  • RDD Persistence
  • Introduction
  • Spark: An Iterative Algorithm
  • Introduction To Graph Parallel System
  • Introduction To Machine Learning
  • Introduction To Three C's
  • Introduction
  • Interoperating with RDDs
  • Introduction To Graph Parallel System
  • Introduction To Machine Learning
  • Introduction To Three C's