CS614 DATA WAREHOUSING

CS614: Data Warehousing

CS614 Data Warehousing is a specialized course that focuses on the design, implementation, and use of Data Warehouses—large, centralized repositories of data that are designed specifically for reporting and data analysis. Unlike a standard operational database (OLTP) which is optimized for fast transactions (like placing an order), a data warehouse (OLAP) is optimized for complex queries that analyze historical data (like "What were the total sales of our top 10 products, by region, for each of the last 12 quarters ? ").

This course is a cornerstone of Business Intelligence (BI). You will learn the entire process of taking raw data from various, heterogeneous sources (e.g., sales, marketing, HR databases), cleaning and transforming it, and loading it into a unified "single source of truth" for decision-making. This process is known as ETL (Extract, Transform, Load).

Key Topics Covered:

• Data Warehouse vs. OLTP: A deep dive into the fundamental differences in purpose, design, workload, and users between operational databases (Online Transaction Processing) and analytical databases (Online Analytical Processing).
• Data Warehouse Architecture: Exploring the components of a BI solution, including data sources, the staging area, the data warehouse itself, and data marts (smaller, department-focused subsets of the warehouse).
• Dimensional Modeling: The core design technique for data warehouses. Unlike normalization (3NF) in OLTP, dimensional modeling uses star schemas and snowflake schemas. You will learn to design fact tables (containing metrics, e.g., 'sales_amount') and dimension tables (containing context, e.g., 'time', 'product', 'location').
• The ETL Process:
  • Extract: Getting data from various source systems.
  • Transform: The most complex step. This involves cleaning data (fixing errors), integrating data (matching 'John Smith' with 'J. Smith'), and transforming it to fit the dimensional model.
  • Load: Efficiently loading the processed data into the warehouse.
• OLAP Operations: Understanding the types of analysis a data warehouse enables, such as slicing, dicing, drill-down, roll-up, and pivot.
• Introduction to Data Mining: A brief overview of how data warehouses serve as the foundation for data mining and discovering hidden patterns in business data.

Course Objectives:

1. Clearly differentiate between OLTP and OLAP systems.
2. Design and implement robust data warehouse schemas using dimensional modeling (star and snowflake schemas).
3. Understand and design the complete ETL (Extract, Transform, Load) pipeline.
4. Explain the business rationale and benefits of data warehousing for decision support.
5. Gain familiarity with OLAP operations and the role of the data warehouse in business intelligence.

CS614 is essential for students interested in careers in data engineering, data analysis, business intelligence, and data science. It provides the skills to build the data foundations that all modern analytics rely on.