Module second : Data Warehousing

 The second module of the course would cover Data Warehousing, which includes the following topics:

1. Introduction to Data Warehousing: This section defines what a data warehouse is and its role in the larger field of data engineering. It also explains the difference between a data warehouse and other data storage systems such as databases and data lakes.
2. Data Warehousing Architecture: This section covers the different types of data warehousing architecture such as the traditional three-tier architecture, the data vault architecture and the Lambda architecture, and how they are used to design and build data warehouses.
3. Data Warehousing Design: This section covers the best practices and techniques for designing and modeling a data warehouse, including data modeling, normalization, and denormalization.
4. Data Warehousing Performance: This section covers the strategies and techniques used to optimize the performance of a data warehouse, including indexing, partitioning, and aggregation.
5. Data Warehousing Tools: This section covers the various tools and technologies used in data warehousing such as SQL, ETL tools, and data warehousing platforms like Amazon Redshift, Google BigQuery, and Azure Synapse Analytics.
6. Data Warehousing in the Cloud: This section covers the design and management of data warehouses in the cloud using AWS, Azure, or GCP.
7. Case studies and real-world examples of data warehousing projects, to give students hands-on experience and a deeper understanding of the concepts covered in the module.
8. Summary: This section provides a summary of the key concepts covered in the module and what students should have learned.

The module is designed to give students a solid understanding of data warehousing concepts, best practices, and techniques, and how to design and implement a data warehouse using different architectures and technologies.