Unit 1
Introduction: Overview of DBMS, File vs DBMS, elements of DBMS. Database design: E-R model, Notations, constraints, cardinality and participation constraints
Course Name | Data Base Management for Big Data |
Course Code | 24CSC333 |
Program | 5 Year Integrated MSc/ BSc. (H) in Mathematics with Minor in Data Science |
Semester | Elective |
Credits | 3 |
Campus | Amritapuri |
Introduction: Overview of DBMS, File vs DBMS, elements of DBMS. Database design: E-R model, Notations, constraints, cardinality and participation constraints
Relational Data Model: Introduction to relational model, Structure of relational mode, domain,keys, tuples to relational models, sql queries.Relational Database Design: Functional dependency, Normalization: 1NF,2NF,3NF,BCNF,table joins.
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 – Non Definitional traits of Big Data – Business Intelligence vs. Big Data – Data warehouse and Hadoop environment – Coexistence.
Big Data Analytics: Classification of analytics – Data Science – 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 – Combiner – Partitioner – Searching – Sorting – Compression. Hadoop 2 (YARN): Architecture – Interacting with Hadoop Eco systems.
Course outcomes
CO 1: Understand the basic concepts of database and bigdata.
CO 2.: Understand the database models and its implementation techniques.
CO 3: Ability to learn big data implementation platforms
CO 4: Ability to learn data base technologies associated with big data.
CO 5: Ability to apply Data Intensive tasks using the Map Reduce Paradigm
Textbooks:
References:
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