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Il y a 3 modules dans ce cours
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.
Inclus
3 vidéos1 lecture2 devoirs
Afficher les informations sur le contenu du module
3 vidéos•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 lecture•Total 12 minutes
Implementing dbt Tests for Production Data Models•12 minutes
2 devoirs•Total 24 minutes
Implement Comprehensive dbt Test Suite for Sales Data Model•18 minutes
Automated Data Quality Testing Validation•6 minutes
Module 2: Analyze Validation Failures to Pinpoint Root Cause
Module 2•1 heure à terminer
Détails du module
Learners will systematically investigate data quality failures using data lineage, system logs, and analytical techniques to identify underlying causes.
Inclus
1 vidéo1 lecture2 devoirs
Afficher les informations sur le contenu du module
1 vidéo•Total 7 minutes
Data Lineage Analysis for Failure Investigation•7 minutes
1 lecture•Total 12 minutes
Systematic Approaches to Data Quality Failure Investigation•12 minutes
2 devoirs•Total 27 minutes
Conduct Root Cause Analysis for Production Data Quality Failure•20 minutes
Root Cause Analysis Methodology Validation•7 minutes
Extend a SQL Validation Framework with Null Pattern and Accepted Value Templates•20 minutes
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