Big Data Analytics

GANPAT UNIVERSITY

FACULTY OF ENGINEERING & TECHNOLOGY

Programme

Bachelor of Technology

Branch/Spec.

Computer Science & Engineering (BDA)

Semester

V

Version

1.0.0.1

Effective from Academic Year

2022-23

Effective for the batch Admitted in

June 2020

Subject  Code

2CSE506

Subject Name

BIG DATA ANALYTICS

Teaching scheme

Examination scheme (Marks)

(Per week)

Lecture(DT)

Practical(Lab.)

Total

CE

SEE

Total

L

TU

P

TW

Credit

3

0

2

0

5

Theory

40

60

100

Hours

3

0

4

0

7

Practical

60

40

100

Pre-requisites:

Database Management System, JAVA/Python Programming Language

Learning Outcomes:

Upon Completion of the course, the students will be able to

• Understand big data phenomena and its  applications
• Understand distributed file system concepts and algorithms
• Implement distributed storage and big data processing concepts using open source tools like Hadoop
• Apply Hadoop ecosystem tools to implement  big data related case studies.

Theory syllabus

Unit

Content

Hrs

1

Distributed file system fundamentals

Introduction to distributed file system, difference between normal file system and distributed file system, benefits and requirement of distributed file system, distributed file system algorithms

4

2

Data Science Basics

Introduction to big data and data analytics and its importance, Characteristics of Big Data, drivers of big data, big data case studies, data science pipeline

5

3

Hadoop Architecture (HDFS, MapReduce, YARN)

introduction to Hadoop Distributed File System (HDFS), HDFS commands, HDFS Architecture, HDFS read and write operations, MapReduce Framework, Map Reduce phases, Failover mechanism, Introduction to YARN, YARN Architecture, Use cases

9

4

Hadoop Ecosystem

Storing and Querying Data using PIg and HIVE, HBase, Slider and Knox, Sqoop

20

5

Hadoop Administration

Key areas of Hadoop Administration, Creating and configuring Hadoop cluster, Apache Ambari, Zookeeper, Security and Governance.

4

6

Enterprise Analytics Tool

Introduction to IBM Watson Studio, Analyzing data with Watson Studio, Big Data tools available in watson studio

3

Practical List

The Practicals will be based on implementing various tasks using Hadoop ecosystem tools - HDFS commands, Pig Latin, Hive, HBASE, Sqoop, Watson Studio Big Data tools

Text Books

1

Hadoop: The Definitive Guide, By Tom White

Reference Books

1

Big Data and Analytics, by Subhashini Chellappan Seema Acharya

2

Big Data Analytics with Hadoop 3 by Sridhar Alla

3

Harness the Power of Big Data The IBM Big Data Platform by  Paul Zikopoulos, Dirk deRoos, Krishnan Parasuraman, Thomas Deutsch, James Giles, David Corrigan

Course Outcomes

COs

Description

CO1

Understand big data phenomena and its  applications

CO2

Understand distributed file system concepts and algorithms

CO3

Implement distributed storage and big data processing concepts using open source tools like Hadoop

CO4

Apply Hadoop ecosystem tools to implement  big data related case studies.

Mapping of CO and PO: