Automate, Analyze, and Validate Data Quality

Overview

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Data quality failures cost organizations millions in bad decisions and lost trust. This advanced course transforms you into a data quality architect who can prevent these failures before they happen. This Short Course was created to help data engineers and analysts accomplish bulletproof data validation automation that catches issues before they impact business decisions. By completing this course, you'll be able to embed automated quality checks directly into your data pipelines, systematically diagnose validation failures to their root cause, and build reusable SQL frameworks that scale across your entire data ecosystem. By the end of this course, you will be able to: - Apply automated data quality tests to data models - Analyze validation failures to pinpoint the root cause - Create a reusable SQL validation framework based on table statistics This course is unique because it focuses on building systematic, code-based validation solutions rather than manual testing approaches, giving you the skills to automate data governance at enterprise scale. To be successful in this project, you should have a background in SQL, data pipeline concepts, and database system fundamentals.

Syllabus

Module 1: Apply Automated Data Quality Tests to Data Models

Learners will implement automated data quality tests directly into data models using SQL-based validation frameworks.

Module 2: Analyze Validation Failures to Pinpoint Root Cause

Learners will systematically investigate data quality failures using data lineage, system logs, and analytical techniques to identify underlying causes.