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Udemy

Learn ETL Testing & Data Warehouse fundamentals

via Udemy

Overview

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.

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.

Syllabus

  • Introduction to ETL & Data WareHouse and their Significance
  • Learn Data Models, ETL , ELT roles in the System Architectural design
  • Setting up artifacts in the ETL tool for building a ETL real time Pipeline
  • Learn how Data is retrieved from different sources in Extraction Phase of ETL
  • Transform Data as per business rules in ETL pipeline and write Test Scenarios
  • Load Data into Data Warehouse as per data model to complete ETL Pipeline design
  • End to End ETL Test Scenarios with associated SQL Queries to test ETL pipeline
  • Learn Slowly Changing Dimensions in data model & derive Test Scenarios of SCD
  • Glossary -Brush up SQL Joins & GroupBy concepts - Course Prerequiste
  • Bonus Lecture

Taught by

Rahul Shetty Academy

Reviews

4.6 rating at Udemy based on 979 ratings

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