Syllabus
Unit 1
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 2
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 vsNewSQL – Introduction to Hadoop – Features – Advantages – Versions
Unit 3
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. 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
- To provide an introduction to big data technologies and tools used for big data
- Familiarize on the basics of relational databases and its implementation strategy using SQL are discussed in the first phase
- Introduce on concepts of big data and its architecture, storage and processing of data in parallel and distributed system
- Analyze unstructured data using NOSQL databases
Course Outcomes
CO
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CO Description
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CO1
|
Identify fundamental concepts of Databases and SQL
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CO2
|
Apply SQL for data storage and retrieval
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CO3
|
Explain fundamental concepts of Big Data and its technologies
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CO4
|
Apply Map reduce programming for big data
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CO5
|
Analyse appropriate NoSQL database techniques for storing and processing large volumes of structured and
unstructured data
|
CO-PO Mapping
|
PO1
|
PO2
|
PO3
|
PO4
|
PO5
|
PO6
|
CO1
|
3
|
1
|
2
|
|
|
2
|
CO2
|
3
|
1
|
2
|
1
|
|
2
|
CO3
|
3
|
1
|
2
|
1
|
|
2
|
CO4
|
3
|
1
|
2
|
1
|
|
2
|
CO5
|
3
|
1
|
2
|
1
|
|
2
|
Skills Acquired
Data Visuvalization skills, programming and data mining skills, quantitative analysis and problem solving skills.