Learn Etl Testing & Data Warehouse Fundamentals
Published 6/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 7h 2m | Size: 5.92 GB
Be a Data Quality Assurance Engineer - Build a strong foundation in ETL, Data Warehousing, and testing for data quality
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.
Requirements
Knowledge on SQL Basics will helpful
Description
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.
Who this course is for
Software Testers
Data Engineers
ETL Testers
Business Intelligence (BI) Professionals
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