Learn ETL Testing & Data Warehouse fundamentals

A hands-on tutorial that takes you from the ground up and gives you a solid understanding of Data Warehouse and ETL Testing concepts. What will you learn from this course? - Learn why and where ETL is required with a real-time business problem. - Understand the fundamentals of Data Warehousing and common data models such as Star Schema. - Gain a complete architectural overview of how ETL works with a Data Warehouse. - Get an overview of popular ETL tools used in the industry. - Build a real-time ETL project from scratch using Pentaho Data Integration (PDI) tool. - Understand the scope of ETL testing at each layer of the pipeline with practical examples. - Learn how to build ETL test scenarios and validate them using SQL queries. - Write test cases for advanced concepts such as Slowly Changing Dimensions (SCDs). - Explore Cloud Data Warehouses and how ETL/ELT fits in modern data stacks. - Understand the differences between ETL vs ELT and where each is applicable. - Discover the critical role of ETL data quality testing in training Large Language Models (LLMs) — ensuring reliable and accurate data pipelines is a key foundation for any AI/ML system. - Learn how bad data quality can lead to hallucinations, bias, and inaccurate results in LLM outputs, and why robust ETL testing is crucial before model ingestion. Prerequisites: - Basic knowledge of SQL (Insert, Update, Delete). - Core SQL concepts such as Joins, Group By, and Subqueries are used frequently in ETL test scenarios. - A refresher on these SQL topics is available in the last section of the course — recommended for those who need it.

What you'll learn:

• Understand ETL & Data Warehouse fundamentals with real-world business case examples.
• Build a complete ETL pipeline using Pentaho Data Integration from scratch.
• Design effective ETL test scenarios using SQL queries for data quality validation.
• Understand the scope of ETL testing at each layer of the pipeline with practical examples
• Learn Slowly Changing Dimensions and how to test them in ETL workflows.
• Explore ETL vs ELT architectures and when to use each in modern data stacks.
• Discover why data quality testing is critical before using data to train LLMs and AI models.