BIG DATA TECHNOLOGIES

Paper Code: 
MCA 322
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

This Course enables the students to

1. Introduce the tools required to manage and analyze big data  

2. Relate data management by RDBMS & NOSQL.

3. Generate applications using map reduce.

4. Develop skills to solve complex real world problems.

 Learning Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

CO94. Select the tools to manage big data.

CO95. Compare different tools used in big data analytics.

CO96. Experiment with data management using NOSQL.

CO97. Develop new applications using map reduce.

Approach in teaching:

Interactive Lectures, Tutorials, Demonstrations, Flipped classes.

 

Learning activities for the students:

Self-learning assignments,

Quizzes, Presentations,

Discussions

 

  • Assignment
  • Written test in classroom
  • Classroom activity
  • Multiple choice questions
  • Semester End Examination

 

10.00
Unit I: 
Understanding Big Data

Introduction, Need, convergence of key trends, structured data Vs. unstructured data , industry examples of big data, web analytics – big data and marketing, fraud and big data, risk and big data, credit risk management, big data and algorithmic trading, big data and its applications in healthcare, medicine, advertising etc.

 

14.00
Unit II: 
Big Data Technologies: Hadoop

Open source technologies,  cloud and big data,  Crowd Sourcing Analytics, inter and trans firewall analytics  Introduction to Hadoop, Data format, analyzing data with Hadoop, scaling out, Hadoop streaming, Hadoop pipes. Design of Hadoop distributed file system (HDFS), HDFS concepts – Java interface, data flow, Data Ingest with Flume and Sqoop. Hadoop I/O – data integrity, compression, serialization, Avro – file-based data structures.

 

14.00
Unit III: 
Hadoop Related Tools

Introduction to Hbase: The Dawn of Big Data, the Problem with Relational Database Systems. Introduction to Cassandra: Introduction to Pig, Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries.

 

10.00
Unit IV: 
NOSQL Data Management

Introduction to NoSQL, aggregate data models, key-value and document data models, relationships, graph databases, schemaless databases, materialized views, distribution models, sharding, master-slave replication, peer-peer replication Consistency: relaxing consistency, version stamps

 

12.00
Unit V: 
Map Reduce Applications

MapReduce workflows, unit tests with MRUnit,  test data and local tests, anatomy of MapReduce job run, classic Map-reduce – YARN,  failures in classic Map-reduce and YARN – job scheduling, shuffle and sort,  task execution, MapReduce types – input formats – output formats, MapReduce – partitioning and combining, Composing MapReduce Calculations.

 

ESSENTIAL READINGS: 

Text Books:

  • Big Data, Black Book, DT Editorial Services, DreamTech Press 2015
  • Professional NOSQL, Shashank  Tiwari, Wrox, September 2011
  • Hadoop in Practice, Alex  Homes, Dreamtech Press, 2015

 

REFERENCES: 

References:

  • HBase: The Definitive Guide, Lars George, O'Reilley, 2011.  
  • Cassandra: The Definitive Guide, Eben Hewitt, O'Reilley, 2010.
  • Programming Pig, Alan Gates, O'Reilley, 2011.  
  • NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, P. J. Sadalage and M. Fowler, Pearson Education, Inc. 2012.
  • Programming Hive, E. Capriolo, D. Wampler, and J. Rutherglen, O'Reilley, 2012

 

Academic Year: 
2021-22
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