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• Multidimensional Databases and Data Warehousing, Christian S. Jensen, Torben Bach Pedersen, Christian Thomsen, Morgan & Claypool Publishers, 2010
• Data Warehouse Design: Modern Principles and Methodologies, Golfarelli and Rizzi, McGraw-Hill, 2009
• Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Elzbieta Malinowski, Esteban Zimányi, Springer, 2008
•  The Data Warehouse Lifecycle Toolkit, Kimball et al., Wiley 1998
• The Data Warehouse Toolkit, 2nd Ed., Kimball and Ross, Wiley, 2002
•  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.
• The Hadoop for Dummies by Dirk deRoos, Paul C.Zikopoulos, Roman B.Melnyk, Bruce Brown, Rafael Coss
• Hadoop in Practice by Alex Holmes, MANNING Publ.
•  Hadoop MapReduce Cookbook, Srinath Perera, Thilina Gunarathne

Big Data Analytics Syllabus IIT

OBJECTIVES:
• Optimize business decisions and create a 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.

UNIT – I:

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

UNIT – II:

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

UNIT – III:

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, Record Reader, Combiner, Partitioner

UNIT – IV:

Hadoop I/O: The Writable Interface, Writable Comparable, and comparators, Writable Classes: Writable wrappers for Java primitives, Text, Bytes Writable, Null Writable, Object Writable
and Generic Writable, Writable collections, Implementing a Custom Writable: Implementing a Raw Comparator for speed, Custom comparators

UNIT – V:

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

UNIT – VI:

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.

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

SOFTWARE LINKS:
1. Hadoop:http://hadoop.apache.org/
2. Hive: https://cwiki.apache.org/confluence/display/Hive/Home
3. Pig Latin: http://pig.apache.org/docs/r0.7.0/tutorial.html

Big Data Analytics Questions List

• Define Wrapper Class? Explain in brief about writable wrappers for java primitives.
• Differentiate between Array List and class linked list functionalities.
•  What are the modes that a Hadoop can run?
• Discuss in brief about the building blocks of Hadoop?
• Describe in brief about API for the Map-reduce framework.
• Discuss in brief about the implementation of the map-reduce concept with a suitable example.
• What are the object writable and generic writable?
• Explain with an example, how Hadoop uses Scale-out feature to improve the performance?
• Discuss in brief about running a pig script in local and distributed mode.
• Describe in brief about PIG Commands

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