??When an ETL fails, Audit Logging saves hours of debugging! Instead of scrambling through job logs, I ensure: ? Error Logging – Every failure is captured in an ErrorLog table with details. ? Automated Alerts – On failure, developers get instant notifications with exact error info captured. Beyond logging, here’s how I prevent failures altogether: ? TRY_CAST for Data Conversion – Prevents failures by handling invalid values gracefully. Instead of failing, invalid data is logged for review. ? Pre-check Validations – Pipeline checks file availability in extract phase and alerts on missing files to prevent failures. A good logging system turns failures into quick fixes! How do you handle ETL failures? #ETL #DataEngineering #SQL #Debugging

Here are the steps I follow, 1. Find the quick fix to keep it active: The broken ETL can impact the next steps. It is better to remove the problematic component first, and make the ETL active. 2. Identify the error place: From the error logs, you can locate the broken part. It happens almost always that the part giving error is working fine. This is the point where you can start back-tracking the issue for a root cause. 3. Develop solution in the safe environment: We find a problem, develop a solution, and apply it without testing. It is best to develop, and test first rather than just deploying. 4. Monitor the new solution: It is important to monitor the pipeline after deploying a solution. This can resolve many issues before happening.

Here's a compact troubleshooting plan for ETL pipeline crashes: * Immediate: * Alerts/Notifications. * Log collection (errors, timestamps). * Pipeline stage/data at failure. * Isolate: * Reproduce in dev/staging. * Divide/test pipeline components. * Check dependencies (DB, network). * Validate data. * Root Cause: * Identify cause (data, code, resources, config). * Document the cause. * Resolve: * Implement fix. * Thorough testing. * Deploy/monitor. * Data recovery. * Post-mortem. * Improve error handling.

"When a data pipeline fails, my first step is to identify the root cause by checking logs and monitoring alerts. I prioritize quick fixes to restore functionality and then implement long-term solutions! to prevent recurrence. For instance,I once encountered a pipeline failure due to a corrupted data file. I quickly isolated the issue, reran the pipeline with a clean file, and later added validation checks to catch such errors early. Also include the try, except and error handling in code.

Troubleshooting an unexpected crash in an ETL (Extract, Transform, Load) pipeline can be a multi-step process, requiring a methodical approach to identify and fix the issue.