Data Quality and Debugging for Reliable Pipelines
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.
56/100
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- What the provider tells you
- 32/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- define automated data quality tests
- trace anomalies back to their source
- apply advanced Python debugging techniques
- write YAML test suites
- navigate monitoring dashboards
- analyze stack traces
- step through live code with debugging tools
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