BIG DATA ANALYTICS

 B.Tech. (VI Sem.) 

20CS19-    BIG DATA ANALYTICS     3 0 0 3 

 Pre-requisite: Database Management Systems, Data Warehousing and Data Mining 

Course Educational Objective: Understanding the process of distributed data (Structured, Semi-Structured and Unstructured) that process the Terabytes of data using Hadoop Eco System Tools. 

 Course Outcomes: At the end of this course, the student will be able to 

 CO1: Identify Big Data and its Business Implications. (Understand - L2) 

 CO2: Process of distributed file system using Hadoop(Apply - L3) 

 CO3: Illustrate the MapReduce mechanism (Apply - L3) 

 CO4: Develop structured data processing tools (Apply- L3) 

 CO5: Develop semi/unstructured data processing tools (Apply – L3) 

 UNIT – I: Introduction to Big data Types of Digital Data, Classification of Digital Data, Characteristics of Data, Evolution of Big Data, Definition of Big Data, Challenges with Big Data, What is Big Data?, Other Characteristics of Data Which are not Definitional Traits of Big Data, Why Big Data?, analyzing Data with Unix tools, Analyzing Data with Hadoop, Hadoop Streaming, Hadoop Echo System. 

Link-https://drive.google.com/file/d/1XTBuazU6J9eDdePe3lp5VKJ3Ms2TwiqE/view?usp=sharing

 UNIT – II: Hadoop Distributed File System The Design of HDFS, HDFS Concepts, Command Line Interface, Hadoop file system interfaces, Data flow, Data Ingestion with Sqoop and Hadoop archives, Hadoop I/O: Compression, Serialization, Avro and File-Based Data structures. 

Link -https://drive.google.com/file/d/1WEsSkGNSqSTjxwjNjymMfhNx3iHx-zQt/view?usp=sharing

 UNIT – III: MapReduce Technique How MapReduce works?, Anatomy of a Map Reduce Job Run, Failures, Job Scheduling, Shuffle and Sort, Task Execution, Map Reduce Types and Formats, Map Reduce Features. 

Link-https://drive.google.com/file/d/1653vPoxvfI9hm1DXFKUrHeB14sg-_yCA/view?usp=sharing

UNIT – IV: Structured Data Processing Tools Hive: Installation, Running Hive, HiveQL, Tables, Querying Data, User Defined functions Sqoop: Introduction, generate code, Database import, working with imported data, Importing large objects, performing an exports 

Link-https://drive.google.com/file/d/1JmimFiUZJIfUN8gfY54U52uTflApF6P1/view?usp=sharing

UNIT – V: Semi-structured and unstructured Data Processing Tools Pig: Introduction to PIG, Execution Modes of Pig, Comparison of Pig with Databases, Grunt, Pig Latin, User Defined Functions, Data Processing operators. HBase: Basics, Concepts, Clients, Example, HBase Versus RDBMS..

Link-https://drive.google.com/file/d/12_GyNB-zKt9VkqpquXMKZVqH8uA6_-kC/view?usp=sharing

  TEXT BOOKS:

 1. Tom White "Hadoop: The Definitive Guide" Third Edit, O'reily Media, 2012. 

 2. Big Data and Analytics, 2ed Seema Acharya, Subhashini Chellappan, Wiley 2015. 

REFERENCE BOOKS: 

1.  Michael Berthold, David J. Hand, "Intelligent Data Analysis", Springer, 2007. 
2.  Jay Liebowitz, "Big Data and Business Analytics" Auerbach Publications, CRC press (2013) 
3.  Tom Plunkett, Mark Hornick, "Using R to Unlock the Value of Big Data: Big Data Analytics with Oracle R Enterprise and Oracle R Connector for Hadoop", McGraw-Hill/Osborne Media (2013), Oracle press. 
4.  Anand Rajaraman and Jefrey David Ulman, "Mining of Massive Datasets", Cambridge University Press, 2012. 
5.  Bill Franks, "Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics", John Wiley & sons, 2012.
6.  Glen J. Myat, "Making Sense of Data", John Wiley & Sons, 2007 
7.  Pete Warden, "Big Data Glossary", O’Reily, 2011. 
8.  Michael Mineli, Michele Chambers, Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley Publications, 2013. 
9.  ArvindSathi, "BigDataAnalytics: Disruptive Technologies for Changing the Game", MC Press, 2012 
10.  Paul Zikopoulos ,Dirk DeRoos , Krishnan Parasuraman , Thomas Deutsch , James Giles, David Corigan, "Harness the Power of Big Data The IBM Big Data Platform", Tata McGraw Hill Publications, 2012.