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.