In this introductory lesson, you’ll design and implement automated data validation tests using SQL, Python, and Great Expectations. You'll define expectations—like uniqueness, null thresholds, and valid value ranges—and apply them to assess data accuracy and completeness in both batch and streaming pipelines. By the end of the lesson, you’ll know how to embed validation logic directly into your development and production workflows, giving your data systems a proactive defense against quality issues.

In this lesson, learners explore how to embed automated data quality checks into ETL and streaming workflows using CI/CD tools like dbt, Airflow, and GitHub Actions. Instead of reacting to data issues downstream, they’ll practice integrating validation logic early—catching schema changes, null floods, and out-of-range values before they break pipelines. Through hands-on activities and guided discussions, learners build scalable, testable workflows that ensure clean data flows reliably through both real-time and batch systems.

In this final lesson, learners will focus on how to move beyond test execution and into ongoing data quality governance. We'll explore strategies for implementing monitoring dashboards, governance policies (like data test SLAs), and collaboration workflows that help teams continuously improve data validation efforts over time. Learners will see how to centralize test results, build accountability into the validation lifecycle, and adapt tests as data and systems evolve. Whether you’re leading a QA team or managing enterprise-scale pipelines, this lesson helps ensure your testing practices remain transparent, sustainable, and reliable.