Advanced Data Testing for Quality at Scale

Overview

Advanced ALM Strategies with Azure DevOps and GitHub Integration is an advanced-level course designed for DevOps engineers, release managers, and software delivery leaders who want to implement scalable, secure, and policy-driven Application Lifecycle Management (ALM) practices. Taught by experienced DevOps professionals, this course equips learners with the tools and strategies needed to optimize software delivery pipelines across complex enterprise environments. Through real-world use cases, scenario-based walkthroughs, hands-on activities, and design challenges, learners will explore advanced branching models, secure CI/CD pipelines, automated quality gates, and governance frameworks using GitHub, Azure DevOps, and supporting integrations. You'll learn to build traceable workflows, enforce compliance and testing standards, and evaluate your DevOps maturity using monitoring and feedback loops. By the end of the course, you’ll have designed a personalized ALM blueprint that aligns delivery speed with security, scale, and compliance—ready to apply directly in your organization.

Syllabus

Lesson 1: Build Trust by Design: Automating Data Validation at Scale

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.

Lesson 2: Automating Data Quality: CI/CD-Ready Validation in Pipelines

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.

Lesson 3: Monitoring, Governance & Continuous Improvement in Data Testing

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.