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From SQL Validation to Production-Ready ELT Test Automation

5 Inputs
1 Hour
Beginner
scenario poster
Industry
general
Skills
approach
quality
data-understanding
data-quality
Tools
databricks
python
sql

Learning Objectives

Design and implement comprehensive data quality validation strategies using complex SQL queries, hierarchical validation rules, and automated reconciliation tests with tolerance levels for production ELT pipelines
Create automated systems that track data dependencies, assess downstream impacts of quality failures, and generate actionable severity reports for complex data warehouse environments
Apply advanced testing techniques on modern data platforms (Databricks/Spark) including distributed processing validation, temporary view testing, and cloud-native best practices

Overview

In today's data-driven economy, data quality failures cost organizations an average of $12.9 million annually. As companies increasingly rely on complex ELT pipelines to power critical business decisions, the demand for skilled data testing professionals has exploded. Yet most data engineers and analysts lack systematic training in testing methodologies, leading to production failures, compliance issues, and lost business opportunities.

This masterclass bridges that critical gap.

What You'll Master**

This intensive, scenario-based program transforms you from someone who hopes data is correct to someone who proves it is.

Why This Matters

  • Business Impact: Learn to prevent costly data quality failures before they reach production
  • Career Acceleration: Master specialized skills that make you indispensable in any data organization
  • Industry Relevance: Solve real-world testing challenges using current tools and best practices
  • Practical Application: Build immediately usable testing frameworks, not just theoretical knowledge

Prerequisites

  • Strong command of SQL including JOINs, subqueries, window functions, CTEs, and aggregation functions - essential for analyzing complex validation scenarios and debugging query logic
  • Understanding of Python syntax, functions, loops, data structures (lists, dictionaries), and ability to read/write simple scripts - required for test automation framework exercises
  • Familiarity with ETL/ELT concepts, data warehousing principles, data flow understanding, and basic knowledge of how data moves through transformation stages