Data pipeline failures cost organizations millions in lost revenue and broken decisions. This course empowers data management professionals with practical skills to build bulletproof data quality systems using industry-standard frameworks and automated testing approaches.

This Short Course was created to help data engineers and analysts accomplish robust data validation that prevents costly pipeline failures and ensures reliable analytics. By completing this course, you'll be able to implement comprehensive data quality tests that automatically catch issues before they impact downstream systems, write YAML-based validation suites that monitor null rates and row counts, and establish automated quality gates that protect your data infrastructure. By the end of this course, you will be able to: Apply a data quality framework to define tests for data integrity Implement automated validation for volume, completeness, and uniqueness requirements Write YAML test suites that enforce quality standards across data pipelines This course is unique because it focuses on practical, hands-on implementation of enterprise-grade data quality frameworks using real-world scenarios and industry-standard tools like Great Expectations and dbt testing. To be successful in this project, you should have a background in basic data concepts, familiarity with SQL queries, and understanding of data pipeline fundamentals.