Data Quality and Debugging for Reliable Pipelines
By External Provider
About 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.
What you will 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.