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BIG Data & Hadoop Course

Online Classes : 30 Hours


Reviews :  


Assignment : 40 Hours


Project : 20 Hours


Access : Lifetime Access


24/7 Support

Course Price: INR 20,000.00

Big Data and Hadoop Introduction

Weekend Batches


# Starts
1 17th Dec 2016 Buy Now
2 24th Dec 2016 Buy Now
3 14th Jan 2017 Buy Now
4 28th Jan 2017 Buy Now

Master Big Data and Hadoop to unlock great career opportunities as a Hadoop developer. Become a Hadoop expert by learning concepts like MapReduce, Yarn, Pig, Hive, Sqoop, Flume, HBase, Oozie. Get real time industry with some of the best Big Data projects and real-life use-cases.

Course Contents

Objective: In this module, you will be able to get big data context, its definition and its integration with Hadoop in terms of storage and processing.

Topics: Big Data definition, Big Data context with case studies, structured vs unstructured, Hadoop basics, Hadoop characteristics, entire Hadoop ecosystem, Hadoop core components, Secondary name node.

Objective: In this module, you will learn the concept of Blocks, Rack awareness, HDFS architecture and a few Hadoop commands. This module also covers HDFS High Availability, HDFS Federation and YARN.

Topics: Blocks, Data Replication and Rack Awareness, HDFS File Write Anatomy, HDFS File Read Anatomy, HDFS Architecture, Common Hadoop Shell Commands, High Availability, HDFS Federation, YARN, Firing our First Map Reduce Job, Checking the output of M/R Job and Understanding the dump of Map Reduce Job.

Objective: In this module, you will learn about different Hadoop modes, configuration files, Split vs Blocks, traditional and Hadoop based distributed computing techniques. You will also learn advanced concepts like Combiners & Partitioners and Shuffle & Sort.

Topics: Hadoop Cluster Modes, Configuration Files, Web URLs, Split vs Blocks, Map Reduce use-cases, solving a problem in Traditional way, Understanding Map Reduce way, Map Reduce Anatomy, Advantages of Map Reduce, and Map Reduce Flow.

Objective: In this module, you will learn the advanced concepts like MR Unit, Counters, Distributed Cache and Joins.

Topics: MR Unit, Counters, Distributed Cache, Joins, Secondary Sort and Total Order Sort.

Objective: In this module, you will be get a complete understanding of PIG & its association with Map Reduce.

Topics: Pig Background, Need for Pig, Pig Vs M/R, Pig Definition, Pig Latin, Pig users, Pig usage at Yahoo, Pig Interaction Modes, Pig program execution, Pig data model, Pig data types, Pig operators and specialized Joins.

Objective: In this module you will learn about Hive Background, Hive comparison with RDBMS and Hive design and architecture.

Topics: Hive Background, Hive Definition, Pig vs Hive, RDBMS vs Hive, Hive components, Hive Architecture, Hive Meta Store, Hive Design, Hive Data Model, Partition and Buckets.

Objective: In this module you will learn about NoSQL Background, Hive comparison with RDBMS and Hive design and architecture.

Topics: NoSQL background and description, Real time scenarios, NoSQL landscapes, HBase definition, HBase characteristics, HBase history, HBase vs. RDBMS, HBase Data Model, HBase Data Model – Graphical representation, HBase Data Model – Logical Vs. Physical representation, Version concepts, Region and Region Servers and Zookeeper.

Objective: In this module, you will learn the concepts of Defining the Flume flow, configuring individual components, configuration with twitter, Flume agent, Source, Sync, configuration of entire Flume set-up.

Topics: Scoop Setup between Hadoop and RDBMS, Exporting Data from Hadoop into RDBMS, Importing Data from RDBMS into Hadoop,importing data from

Objective: In this module, you will learn the concepts of Oozie as a Hadoop Workflow Framework and how it orchestrates the execution of Hadoop Components.

Topics: Oozie workflow, Oozie server , Oozie co-ordinator, Oozie Bundles, Configuration XML and Properties file, Creating Oozie application, Oozie web console, Oozie scheduling.

Objective: In this session, you will get familiar with the project you would be working for your certification along with several other topics.

Topics: Project Discussions, Evaluating Individual Approaches, Finalizing Optimal Approach.