Automate, Analyze, and Validate Data Quality
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Automate, Analyze, and Validate Data Quality
This course is part of SQL at Scale: Querying, Transforming, and Governing Specialization
Instructor: Hurix Digital
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What you'll learn
Automated data quality testing embedded in pipelines prevents downstream issues more effectively than reactive manual checks.
Systematic root cause analysis requires methodical investigation of data processes, transformation logic, and system logs beyond surface errors.
Reusable SQL validation frameworks built on database metadata enable scalable data governance across entire enterprise data ecosystems.
Proactive data quality architecture creates trust in data-driven systems and prevents costly business decisions based on corrupted information.
Details to know
April 2026
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There are 3 modules in this course
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.
Learners will implement automated data quality tests directly into data models using SQL-based validation frameworks.
What's included
3 videos1 reading2 assignments
3 videos•Total 13 minutes
- Why Data Quality Automation Prevents Million-Dollar Mistakes•2 minutes
- Essential Components of Automated Data Quality Testing•5 minutes
- Building Automated dbt Tests for Customer Data Models•6 minutes
1 reading•Total 12 minutes
- Implementing dbt Tests for Production Data Models•12 minutes
2 assignments•Total 24 minutes
- Implement Comprehensive dbt Test Suite for Sales Data Model•18 minutes
- Automated Data Quality Testing Validation•6 minutes
Learners will systematically investigate data quality failures using data lineage, system logs, and analytical techniques to identify underlying causes.
What's included
1 video1 reading2 assignments
1 video•Total 7 minutes
- Data Lineage Analysis for Failure Investigation•7 minutes
1 reading•Total 12 minutes
- Systematic Approaches to Data Quality Failure Investigation•12 minutes
2 assignments•Total 27 minutes
- Conduct Root Cause Analysis for Production Data Quality Failure•20 minutes
- Root Cause Analysis Methodology Validation•7 minutes
Learners will build scalable SQL validation frameworks that automatically generate quality checks using database metadata and statistical analysis.
What's included
3 videos1 reading3 assignments1 ungraded lab
3 videos•Total 15 minutes
- Why Reusable Validation Frameworks Scale Data Quality Across Organizations•3 minutes
- Building SQL Validation Templates Using Database Metadata•7 minutes
- Creating Automated SQL Validation Framework with Statistical Analysis•5 minutes
1 reading•Total 12 minutes
- Metadata-Driven SQL Validation Framework Architecture•12 minutes
3 assignments•Total 35 minutes
- SQL Validation Framework Mastery Assessment•12 minutes
- Build Enterprise SQL Validation Framework with Statistical Thresholds•20 minutes
- SQL Validation Framework Fundamentals Knowledge Check•3 minutes
1 ungraded lab•Total 20 minutes
- Extend a SQL Validation Framework with Null Pattern and Accepted Value Templates•20 minutes
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