Course Title

Data Warehouse

Course Number

10672350

Instructor Name

MOAMIN BURHAM JAMIL ABUGHAZALA

Contact Information

[email protected]

Semester(s) and academic year(s)

Second Semester 2025

Compulsory / Elective

Compulsory

This course provides students with a comprehensive foundation in data warehousing, equipping them with the skills needed to design, implement, and optimize data warehouse infrastructures for business intelligence applications. The course covers fundamental concepts in data architecture, data modeling, ETL (Extract, Transform, Load) processes, and data warehouse implementation.

Students will gain hands-on experience with SQL Server Management Studio (SSMS) and learn how to structure and query data warehouses efficiently. Key topics include dimensional modeling (Star and Snowflake schemas), data integration techniques, data security, and governance strategies. The course also explores advanced topics such as real-time data processing, cloud data warehousing, and the integration of machine learning techniques for optimization.

Through practical exercises and projects, students will learn how to extract insights from business data using visualization tools like Power BI and Tableau. By the end of the course, students will be prepared to design and maintain efficient data warehouses that support decision-making and analytics in modern enterprises.

The primary objective of this course is to equip students with the knowledge and hands-on experience required to design, implement, and optimize data warehouse infrastructures for business intelligence and analytics. Specifically, the course aims to:

1. Introduce Fundamental Concepts of Data Warehousing

   • Explain the role of data warehouses in modern business intelligence and analytics.
   • Differentiate between various data architectures, including Data Lakes, Data Warehouses, and Data Lakehouses.
   • Explore the key components of a data warehouse, including ETL, metadata, and query tools.

2. Develop Practical Skills in SQL and Database Management

   • Teach students how to navigate and use SQL Server Management Studio (SSMS).
   • Enable students to write SQL queries for data retrieval, transformation, and optimization in a data warehouse environment.

3. Teach Data Modeling Techniques for Data Warehousing

   • Explain the principles of dimensional modeling, including facts and dimensions.
   • Provide hands-on experience in designing and implementing Star and Snowflake schemas.
   • Guide students in creating time dimension tables and understanding data warehouse structures.

4. Implement ETL Processes for Data Integration

   • Introduce ETL workflows and data transformation techniques.
   • Teach students how to extract, transform, and load (ETL) data using Python (Pandas).
   • Discuss common challenges in data integration and best practices for data cleaning and transformation.

5. Optimize and Secure Data Warehouses

   • Explore indexing and partitioning strategies to improve data warehouse performance.
   • Teach SQL query optimization techniques for efficient data retrieval.
   • Emphasize data security, access control, and governance in data warehousing.

6. Introduce Business Intelligence (BI) and Analytics Tools

   • Demonstrate the use of BI tools such as Power BI, Tableau, and Looker for data visualization.
   • Explain OLAP (MOLAP, ROLAP, HOLAP) and Data Mart concepts for business intelligence applications.
   • Guide students in processing real-time and streaming data for analytics.

7. Explore Performance Tuning and Cloud Data Warehousing

   • Teach students about performance tuning techniques such as materialized views and caching.
   • Explain the benefits of using columnar databases for analytical workloads.
   • Introduce cloud-based data warehousing solutions and their advantages.

8. Expose Students to Advanced Topics in Data Warehousing

   • Demonstrate how machine learning can be used to enhance ETL processes and query optimization.
   • Introduce data quality monitoring techniques and metadata management using tools like Great Expectations and Apache Atlas.

By achieving these objectives, students will develop a strong foundation in data warehousing and business intelligence, preparing them for careers in data engineering, analytics, and enterprise data management.

By the end of this course, students will be able to:

1. Understand Data Architecture Concepts

   • Explain the fundamental concepts of data, information, and knowledge.
   • Describe different data storage formats and processing types (e.g., Lambda, Kappa architectures).
   • Differentiate between modern data architectures such as Data Lakes, Data Warehouses, and Data Lakehouses.

2. Use SQL Server Management Studio (SSMS)

   • Navigate and utilize SSMS tools, including Object Explorer and Visual Database Tools.
   • Write and execute SQL queries for database management.

3. Design and Model Data Warehouses

   • Explain the purpose and evolution of data warehousing.
   • Compare and contrast OLTP and OLAP systems.
   • Design and implement dimensional models using Star and Snowflake schemas.
   • Apply best practices in creating time dimension tables.

4. Implement ETL Pipelines

   • Describe ETL processes and workflows.
   • Extract, transform, and load data using Python (Pandas).
   • Address common data integration challenges in data warehousing.

5. Query and Optimize Data Warehouses

   • Implement indexing and partitioning strategies for performance optimization.
   • Write optimized SQL queries for data retrieval and reporting.
   • Ensure data security and governance in data warehouse implementations.

6. Apply Business Intelligence and Analytics

   • Utilize BI tools like Power BI, Tableau, and Looker for data visualization.
   • Implement OLAP and Data Mart concepts (MOLAP, ROLAP, HOLAP).
   • Process real-time streaming data for analytics.
   • Integrate NoSQL technologies with data warehouses for big data analytics.

7. Optimize and Enhance Data Warehousing Performance

   • Apply performance tuning techniques such as materialized views and caching.
   • Utilize columnar databases for efficient data storage and retrieval.
   • Deploy cloud-based data warehousing solutions.

8. Explore Advanced Topics in Data Warehousing

   • Integrate machine learning techniques to enhance ETL and query optimization.
   • Implement data quality monitoring using tools like Great Expectations and Apache Atlas.

By achieving these ILOs, students will gain the technical and analytical skills necessary to build, maintain, and optimize data warehousing solutions for business intelligence and analytics.

• The Data Warehouse Toolkit
• Rebuilding Reliable Data Pipelines Through Modern Tools