CCS341: Data Warehousing syllabus for AI&DS 2021 regulation (Professional Elective-III)

Data Warehousing detailed syllabus for Artificial Intelligence & Data Science (AI&DS) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the AI&DS students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-III scheme and its subjects refer to AI&DS Professional Elective-III syllabus scheme. The detailed syllabus of data warehousing is as follows.

Course Objectives:

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Unit I

INTRODUCTION TO DATA WAREHOUSE
Data warehouse Introduction – Data warehouse components- operational database Vs data warehouse – Data warehouse Architecture – Three-tier Data Warehouse Architecture -Autonomous Data Warehouse- Autonomous Data Warehouse Vs Snowflake – Modern Data Warehouse

Unit II

ETL AND OLAP TECHNOLOGY
What is ETL – ETL Vs ELT – Types of Data warehouses – Data warehouse Design and Modeling -Delivery Process – Online Analytical Processing (OLAP) – Characteristics of OLAP – Online Transaction Processing (OLTP) Vs OLAP – OLAP operations- Types of OLAP- ROLAP Vs MOLAP Vs HOLAP.

Unit III

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Unit IV

DIMENSIONAL MODELING AND SCHEMA
Dimensional Modeling- Multi-Dimensional Data Modeling – Data Cube- Star Schema- Snowflake schema- Star Vs Snowflake schema- Fact constellation Schema- Schema Definition – Process Architecture- Types of Data Base Parallelism – Datawarehouse Tools

Unit V

SYSTEM & PROCESS MANAGERS
Data Warehousing System Managers: System Configuration Manager- System Scheduling Manager – System Event Manager – System Database Manager – System Backup Recovery Manager – Data Warehousing Process Managers: Load Manager – Warehouse Manager- Query Manager – T uning – T esting

Practical Exercises:

1. Data exploration and integration with WEKA
2. Apply weka tool for data validation
3. Plan the architecture for real time application
4. Write the query for schema definition
5. Design data ware house for real time applications
6. Analyse the dimensional Modeling
7. Case study using OLAP
8. Case study using OTLP
9. Implementation of warehouse testing.

Course Outcomes:

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Text Books:

1. Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAP”, Tata McGraw – Hill Edition, Thirteenth Reprint 2008.
2. Ralph Kimball, “The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling”, Third edition, 2013.

Reference Books:

1. Paul Raj Ponniah, “Data warehousing fundamentals for IT Professionals”, 2012.
2. K.P. Soman, ShyamDiwakar and V. Ajay “Insight into Data mining Theory and Practice”, Easter Economy Edition, Prentice Hall of India, 2006.

For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.