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Data Quality and Debugging for Reliable Pipelines

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Data Quality and Debugging for Reliable Pipelines

This course is part of Open source Data Engineering with Spark, dbt & Airflow Professional Certificate

Instructor: Professionals from the Industry

Included with

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8 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

8 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Define and automate data quality tests using YAML to validate row counts, null thresholds, and uniqueness across pipeline datasets.
Trace data anomalies through pipeline stages by analyzing logs and dashboards to identify and fix the exact source of failure.
Apply advanced Python debugging tools — including conditional breakpoints, watchpoints, and pdb — to diagnose and resolve pipeline issues.
Resolve complex concurrency bugs by reading stack traces and correlating thread logs to identify deadlocks and race conditions in code.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your Data Analysis expertise

This course is part of the Open source Data Engineering with Spark, dbt & Airflow Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Coursera

There are 8 modules in this course

You'll build the diagnostic and preventive skills that keep data pipelines trustworthy and production-ready. In this course, you'll learn to define automated data quality tests, trace anomalies back to their source, and apply advanced Python debugging techniques to resolve complex pipeline failures — three capabilities that employers consistently seek in data engineering roles.

What sets this course apart is its end-to-end, practical focus: you won't just learn what data quality means — you'll write YAML test suites, navigate monitoring dashboards, analyze stack traces, and step through live code with debugging tools. Each skill builds toward a complete picture of pipeline reliability, from prevention to detection to resolution. By the end, you'll be equipped to catch data issues before they reach downstream consumers, communicate root causes clearly, and ship more dependable data products.

Module details

You will establish foundational understanding of data quality frameworks and define systematic approaches to testing data integrity through volume, completeness, and uniqueness validation.

What's included

3 videos1 reading1 assignment

3 videosTotal 15 minutes

Why Data Quality Frameworks Prevent Million-Dollar Pipeline Failures2 minutes
Essential Components of Data Quality Frameworks7 minutes
Implementing Basic Data Quality Tests with SQL6 minutes

1 readingTotal 8 minutes

Data Quality Testing Patterns and Implementation Strategies8 minutes

1 assignmentTotal 3 minutes

Data Quality Framework Foundation Knowledge Check3 minutes

You will implement automated data quality testing using YAML configuration and industry-standard tools to create production-ready validation systems with quality gates and monitoring capabilities.

What's included

2 videos3 readings2 assignments1 ungraded lab

2 videosTotal 12 minutes

How Automated Testing Saves Data Engineers from Midnight Crisis Calls4 minutes
Production-Ready Testing with dbt and Great Expectations9 minutes

3 readingsTotal 25 minutes

YAML-Based Testing Configuration and Great Expectations Integration7 minutes
Building YAML Test Suites for Production Validation8 minutes
Automated Data Pipeline Deployment10 minutes

2 assignmentsTotal 18 minutes

Automated Testing Implementation Mastery Check3 minutes
Data Quality Framework Mastery Assessment15 minutes

1 ungraded labTotal 18 minutes

Automated Data Pipeline Deployment with GitHub Actions18 minutes

You will learn systematic root cause analysis methodology for data pipeline anomalies through monitoring dashboard analysis and methodical investigation techniques.

What's included

1 video2 readings1 assignment1 ungraded lab

1 videoTotal 8 minutes

Data Quality Investigation Framework: From Monitoring to Root Cause 8 minutes

2 readingsTotal 18 minutes

Monitoring Dashboard Analysis: Reading the Signs of Pipeline Distress 10 minutes
Navigating Monitoring Dashboards to Identify Data Anomaly Patterns8 minutes

1 assignmentTotal 3 minutes

Data Quality Investigation Fundamentals Assessment 3 minutes

1 ungraded labTotal 18 minutes

Systematic Data Pipeline Anomaly Investigation18 minutes

You will implement effective resolution strategies for pipeline integrity through targeted fixes, validation techniques, and systematic restoration procedures.

What's included

2 videos2 readings2 assignments

2 videosTotal 16 minutes

When Pipeline Fixes Become Production Heroes 5 minutes
Pipeline Anomaly Resolution: A Structured Approach 11 minutes

2 readingsTotal 18 minutes

Targeted Fix Implementation: SQL Solutions and Pipeline Restoration 10 minutes
Implementing SQL Fixes and Validating Pipeline Restoration 8 minutes

2 assignmentsTotal 16 minutes

Pipeline Resolution Strategy Validation3 minutes
Comprehensive Data Pipeline Troubleshooting Assessment 13 minutes

You will learn systematic debugging approaches using conditional breakpoints, memory inspection, and methodical analysis techniques to transform from trial-and-error debugging to efficient problem resolution in Python data pipelines.

What's included

3 videos1 reading2 assignments

3 videosTotal 14 minutes

When Production Pipelines Fail: The Cost of Poor Debugging3 minutes
Advanced Debugging Fundamentals for Python Pipelines6 minutes
Setting Up Conditional Breakpoints in Production Code5 minutes

1 readingTotal 10 minutes

Conditional Breakpoints and Memory Inspection Techniques10 minutes

2 assignmentsTotal 18 minutes

Hands-on Conditional Debugging in Multi-Batch Pipeline15 minutes
Advanced Debugging Techniques Knowledge Check3 minutes

You will develop systematic approaches to interpret complex stack traces, correlate log patterns, and reconstruct failure scenarios in multithreaded Python environments to identify concurrency issues like deadlocks and race conditions.

What's included

3 videos1 reading2 assignments1 ungraded lab

3 videosTotal 17 minutes

The Hidden Complexity of Multithreaded Debugging4 minutes
Understanding Stack Traces in Multithreaded Environments6 minutes
Analyzing ThreadPoolExecutor Stack Traces for Deadlock Detection7 minutes

1 readingTotal 10 minutes

Log Correlation Techniques for Multithreaded Systems10 minutes

2 assignmentsTotal 13 minutes

Multithreaded Debugging Analysis Knowledge Check3 minutes
Production Multithreaded Debugging Mastery Assessment10 minutes

1 ungraded labTotal 20 minutes

Stack Trace Detective: Debugging Multithreaded Pipeline Failures20 minutes

You will create a comprehensive data quality monitoring system by building automated tests, investigating data anomalies, and debugging complex pipeline issues. This project integrates data quality frameworks, root cause analysis techniques, and advanced debugging skills into a single, production-ready solution.

What's included

4 readings1 assignment

You will explore how generative AI tools enhance data engineering workflows across DevOps practices, performance optimization, and quality assurance. You will discover practical applications of AI assistance in version control, containerization, CI/CD automation, query tuning, and debugging.