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Learn ETL Testing & Data Warehouse fundamentals
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Learn ETL Testing & Data Warehouse fundamentals

About this course

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.

B

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What you'll learn

  • Understand the role and importance of ETL in data processes
  • Gain an architectural overview of how ETL integrates with Data Warehousing
  • Build and implement a real-time ETL project using Pentaho Data Integration
  • Create and validate ETL test scenarios using SQL
  • Differentiate between ETL and ELT practices

Course objectives

  • Provide a solid foundation in Data Warehousing concepts
  • Highlight the importance of ETL data quality testing
  • Enable practical experience through project-based learning
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