Rahul Mondal

This course provides a comprehensive introduction to big data and databases, covering both theoretical concepts and practical applications. Students will learn about various data models, query languages, transaction management, physical design considerations, and popular database systems used in modern computing environments.

Course Outline:

1. Introduction to Big Data and Databases:

   • Overview of big data concepts, challenges, and opportunities.
   • Introduction to database management systems (DBMS) and their role in handling large volumes of data.

2. Conceptual Data Model:

   • Understanding conceptual data modeling techniques for designing database schemas.
   • Entity-relationship modeling and other conceptual modeling approaches.

3. Relational Data Model:

   • In-depth exploration of the relational data model.
   • Relational algebra and relational calculus for querying relational databases.

4. SQL - Structured Query Language (CAI):

   • Introduction to SQL for database querying and manipulation.
   • Basic SQL commands, including SELECT, INSERT, UPDATE, DELETE, and JOIN operations.

5. SQL2 - Advanced SQL (CAI):

   • Advanced SQL topics, such as subqueries, views, triggers, and stored procedures.
   • Optimization techniques and performance tuning strategies for SQL queries.

6. Transactions (CAI):

   • Understanding transaction management concepts in database systems.
   • ACID properties, concurrency control, and isolation levels for maintaining data consistency.

7. Physical Design and Secondary Index (CAI):

   • Physical database design considerations, including indexing strategies and data organization techniques.
   • Introduction to secondary indexing and its role in improving query performance.

8. PostgreSQL (CAI):

   • Overview of PostgreSQL, a powerful open-source relational database management system.
   • Postgres internal query execution, optimization and evaluation strategies.

9. NoSQL (CAI):

   • Introduction to NoSQL databases and their use cases in modern applications.
   • Comparison of different NoSQL database types, including document-oriented, key-value, and column-family databases.

10. Neo4j (CAI):

    • Introduction to Neo4j, a popular graph database management system.
    • Understanding graph data modeling and querying using the Cypher query language.

11. MongoDB (CAI):

    • Overview of MongoDB, a leading document-oriented NoSQL database.
    • Hands-on experience with MongoDB installation, data modeling, and CRUD operations.

12. Redis (CAI):

    • Introduction to Redis, an in-memory data structure store used as a database, cache, and message broker.
    • Understanding Redis data structures and practical use cases.

13. Hadoop (CAI):

    • Introduction to Apache Hadoop, a distributed processing framework for big data.
    • Overview of Hadoop ecosystem components, including HDFS, MapReduce, and YARN.

14. HBase (CAI):

    • Overview of Apache HBase, a distributed, scalable, and consistent NoSQL database built on top of Hadoop.
    • Understanding HBase data model, architecture, and integration with Hadoop ecosystem.