Applied Data Engineering Course (online)

On successful completion of this course, trainees will be able to:

1. Demonstrate a comprehensive understanding of essential data analytics topics, establishing a strong foundation for further exploration and application in the field of data engineering.

2. Apply Data Warehousing Concepts to design and construct efficient data warehouses, including the ability to articulate and implement key architectural principles.

3. Execute data sourcing and cleaning processes effectively, utilizing scripting techniques to ensure the accuracy and reliability of data integrated into the warehouse.

4. Develop practical skills in ETL (Extract, Transform, Load) processes using industry-standard tools such as SSIS and Talend, enabling trainees to design and implement robust data pipelines.

5. Implement advanced testing techniques in ETL processes, ensuring the quality and integrity of data transformations, and demonstrate proficiency in configuration management for maintaining the reliability of the data pipeline.

1. Introduction to essential data analytics topics
2. Data Warehousing Concepts
3. Data Warehousing Architecture
4. Data Warehousing Design: Building Blocks
5. Data Sourcing
6. Data Cleaning
7. Script Requirements for Data Sourcing
8. Metadata
9. Data Integration
10. File Validation
11. The Staging Layer
12. Business Validation Layer
13. Data Warehouse Layer
14. Developing ETL with SSIS
15. Developing ETL with Talend
16. Testing Techniques in ETL
17. Mapping examples
18. Audit, Balance and Control
19. Configuration Management
20. Implementing ChatGPT in Python
21. Reviewing the techniques of Prompt Engineering and Generative AI
22. Final Project: Applying Data Engineering techniques to  real datasets and building the required ETL pipeline