Course code: PCC-CSE-404G
Category: Professional Core Course
Course title: Big Data Analytics
Scheme and Credits
| L | T | P | Credits | |
|---|---|---|---|---|
| Semester 8 | 3 | 0 | 0 | 3 |
| Marks | |
|---|---|
| Class work | 25 |
| Exam | 75 |
| Total | 100 |
| Duration of Exam | 03 Hours |
Objectives of the course:
- To Provide an explanation of the architectural components and programming models used for scalable big data analysis.
- To Identify the frequent data operations required for various types of data and Apply techniques to handle streaming data.
- To describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications.
- To Identify describe and differentiate between relational and non-relational database and how Data Warehouses, Data Marts, Data Lakes, and Data Pipelines work.
- Explain how the Extract, Transform, and Load process works to make raw data ready for analysis.
Note: Examiner will set nine questions in total. Question one will be compulsory. Question one will have 6 parts of 2.5 marks each from all units and remaining eight questions of 15 marks each to be set by taking two questions from each unit. The students have to attempt five questions in total, first being compulsory and selecting one from each unit.
UNIT 1
Introduction to Big Data
Big Data: Why and Where, Application and Challenges, Characteristics of Big Data and Dimensions of Scalability, The Six V, Data Science: Getting Value out of Big Data, Steps in the Data science process, Foundations for Big Data Systems and Programming, Distributed file systems
UNIT 2
Data Repositories and Big Data Platforms
RDBMS, NoSQL, Data Marts, Data Lakes, ETL, and Data Pipelines, Foundations of Big Data, Big Data Processing Tools, Modern Data Ecosystem, Key Players, Types of Data, Understanding Different Types of File Formats, Sources of Data Using Service Bindings
UNIT 3
Introduction to Big Data Modeling and Management
Data Storage, Data Quality, Data Operations, Data Ingestion, Scalability and Security Traditional DBMS and Big Data Management Systems, Real Life Applications, Data Model: Structure, Operations, Constraints, Types of Big Data Model
UNIT 4
Big Data Integration and processing
Big Data Processing, Retrieving: Data Query and retrieval, Information Integration, Big Data Processing pipelines, Analytical operations, Aggregation operation, High level Operation, Tools and Systems: Big Data workflow Management
Suggested Text Books
- Seema Acharya, Subhasini Chellappan, "Big Data Analytics" Wiley 2015.
Suggested Reference Books
- Michael Berthold, David J. Hand, "Intelligent Data Analysis”, Springer, 2007.
- Jay Liebowitz, “Big Data and Business Analytics” Auerbach Publications, CRC press (2013)
- 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.
- Anand Rajaraman and Jef rey David Ulman, “Mining of Massive Datasets”, Cambridge University Press, 2012.
- Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics”, John Wiley & sons, 2012.
- Glen J. Myat, “Making Sense of Data”, John Wiley & Sons, 2007
- Pete Warden, “Big Data Glossary”, O’Reily, 2011.
- Michael Mineli, Michele Chambers, Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley Publications, 2013.
- ArvindSathi, “BigDataAnalytics: Disruptive Technologies for Changing the Game”, MC Press, 2012
- 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.
Course Outcomes
- Understand the architectural components and programming models for scalable big data analysis.
- Identify frequent data operations and apply techniques for streaming data.
- Connect data management operations with big data processing patterns.
- Differentiate between relational and non-relational databases.
- Understand the ETL process for preparing raw data for analysis.