JNTUK B.Tech Hadoop and Big Data (Elective – II) for R13 Batch.

JNTUK B.Tech Hadoop and Big Data (Elective – II) gives you detail information of Hadoop and Big Data (Elective – II) R13 syllabus It will be help full to understand you complete curriculum of the year.

Course Objectives:

• Optimize business decisions and create competitive advantage with Big Data analytics
• Introducing Java concepts required for developing map reduce programs
• Derive business benefit from unstructured data
• Imparting the architectural concepts of Hadoop and introducing map reduce paradigm
• To introduce programming tools PIG & HIVE in Hadoop echo system.

Course Outcomes

• Preparing for data summarization, query, and analysis.
• Applying data modelling techniques to large data sets
• Creating applications for Big Data analytics
• Building a complete business data analytic solution

Unit 1: Data structures in Java: Linked List, Stacks, Queues, Sets, Maps; Generics: Generic classes and Type parameters, Implementing Generic Types, Generic Methods, Wrapper Classes, Concept of Serialization.

Reference: Big Java 4th Edition, Cay Horstmann, Wiley John Wiley & Sons, INC

Unit 2: Working with Big Data: Google File System, Hadoop Distributed File System (HDFS) – Building blocks of Hadoop (Namenode, Datanode, Secondary Namenode, JobTracker, Task Tracker), Introducing and Configuring Hadoop cluster (Local, Pseudo-distributed mode, Fully Distributed mode), Configuring XML files.

References: Hadoop: The Definitive Guide by Tom White, 3rd Edition, O’reilly Hadoop in Action by Chuck Lam, MANNING Publ.

Unit 3: Writing MapReduce Programs: A Weather Dataset, Understanding Hadoop API for MapReduce Framework (Old and New), Basic programs of Hadoop MapReduce: Driver code, Mapper code, Reducer code, RecordReader, Combiner, Partitioner

Reference: Hadoop: The Definitive Guide by Tom White, 3rd Edition, O’reilly

Unit 4: Hadoop I/O: The Writable Interface, WritableComparable and comparators, Writable Classes: Writable wrappers for Java primitives, Text, BytesWritable, NullWritable, ObjectWritable and GenericWritable, Writable collections, Implementing a Custom Writable: Implementing a RawComparator for speed, Custom comparators

Reference: Hadoop: The Definitive Guide by Tom White, 3rd Edition, O’reilly

Unit 5: Pig: Hadoop Programming Made Easier Admiring the Pig Architecture, Going with the Pig Latin Application Flow, Working through the ABCs of Pig Latin, Evaluating Local and Distributed Modes of Running Pig Scripts, Checking out the Pig Script Interfaces, Scripting with Pig Latin

Reference: Hadoop for Dummies by Dirk deRoos, Paul C.Zikopoulos, Roman B.Melnyk,Bruce Brown, Rafael Coss

Unit 6: Applying Structure to Hadoop Data with Hive: Saying Hello to Hive, Seeing How the Hive is Put Together, Getting Started with Apache Hive, Examining the Hive Clients, Working with Hive Data Types, Creating and Managing Databases and Tables, Seeing How the Hive Data Manipulation
Language Works, Querying and Analyzing Data

References: Hadoop for Dummies by Dirk deRoos, Paul C.Zikopoulos, Roman B.Melnyk,Bruce Brown, Rafael Coss.

Text Books

• Big Java 4th Edition, Cay Horstmann, Wiley John Wiley & Sons, INC
• Hadoop: The Definitive Guide by Tom White, 3rd Edition, O’reilly
• Hadoop in Action by Chuck Lam, MANNING Publ.
• Hadoop for Dummies by Dirk deRoos, Paul C.Zikopoulos, Roman B.Melnyk,Bruce Brown, Rafael Coss

References

• Hadoop in Practice by Alex Holmes, MANNING Publ.
• Hadoop MapReduce Cookbook,Srinath Perera, Thilina Gunarathne
• Software Links:
• Hadoop:http://hadoop.apache.org/
• Hive: https://cwiki.apache.org/confluence/display/Hive/Home
• Piglatin: http://pig.apache.org/docs/r0.7.0/tutorial.html

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