Syllabus
Professional Electives
Other Branches
Unit I
Introduction: Overview of DBMS, File vs DBMS, elements of DBMS, Relational Data Model: Introduction to relational model, Structure of relational mode, domain, keys, tuples to relational models. SQL – table creation, relationships, basic queries DML and DDL, Joins, Grouping.
Unit II
Introduction to Big Data: Types of Digital Data – Characteristics of Data – Evolution of Big Data – Definition of Big Data – Challenges with Big Data-3Vs of Big Data -Terminologies in Big Data – CAP Theorem – BASE Concept. NoSQL: Types of Databases – Advantages – NewSQL – SQL vs. NOSQL vs NewSQL. Introduction to Hadoop: Features – Advantages – Versions – Overview of Hadoop Eco systems – Hadoop distributions – Hadoop vs. SQL – RDBMS vs. Hadoop – Hadoop Components – Architecture – HDFS – Map Reduce: Mapper – Reducer – – Map Reduce: Mapper – Reducer – Combiner – Partitioner. Hadoop 2 (YARN): Architecture – Interacting with Hadoop Eco systems.
Unit III
No SQL databases: Cassandra: Introduction – Features – Data types – CQLSH – Key spaces – CRUD operations – Collections – Counter – TTL – Alter commands – Import and Export – Querying System tables.
Objectives and Outcomes
Course Objectives
- The aim of this course is to provide an introduction to big data technologies and tools used for big data.
- Basics of relational databases and its implementation strategy using SQL are discussed in the first phase.
- The second phase discusses on concepts big data and its architecture, storage and processing of data in parallel and distributed system.
- In the last phase retrieval and analysis of unstructured data are done using NOSQL databases.
Course Outcomes
CO1: Understand fundamental concepts of Databases and SQL.
CO2: Apply SQL for data storage and retrieval.
CO3: Understand fundamental concepts of Big Data and its technologies.
CO4: Apply Map reduce programming for big data.
CO5: Analyze appropriate NoSQL database techniques for storing and processing large volumes of structured and
unstructured data.
CO-PO Mapping
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PO/PSO
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PO1
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PO2
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PO3
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PO4
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PO5
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PO6
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PO7
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PO8
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PO9
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PO10
|
PO11
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PO12
|
PSO1
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PSO2
|
|
CO
|
|
CO1
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3
|
2
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1
|
|
|
|
|
|
|
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|
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3
|
2
|
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CO2
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1
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3
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2
|
|
|
|
|
|
|
|
|
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3
|
2
|
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CO3
|
|
2
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2
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1
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3
|
|
|
|
|
|
|
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3
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2
|
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CO4
|
|
3
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2
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2
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3
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|
|
|
|
|
|
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3
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2
|
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CO5
|
|
|
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2
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3
|
|
|
|
|
|
|
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3
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2
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Evaluation Pattern
Evaluation Pattern: 70:30
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Assessment
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Internal
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End Semester
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MidTerm Exam
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20
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Continuous Assessment – Theory (*CAT)
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10
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Continuous Assessment – Lab (*CAL)
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40
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**End Semester
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30 (50 Marks; 2 hours exam)
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*CAT – Can be Quizzes, Assignments, and Reports
*CAL – Can be Lab Assessments, Project, and Report
**End Semester can be theory examination/ lab-based examination/ project presentation
Text Books / References
Textbook(s)
Seema Acharya, Subhashini Chellappan. “Big Data and Analytics”, Wiley Publication; 2015.
Reference(s)
Hurwitz JS, Nugent A, Halper F, Kaufman M. “Big data for dummies”. John Wiley & Sons; 2013.
White T. “Hadoop: The definitive guide”, O’Reilly Media, Inc.”; 2012.
Bradberry R, Lubow E. “Practical Cassandra: a developer’s approach”. Addison-Wesley; 2013.
