1. First thing I do is pause all downstream processes and grab a backup of my current data state. 2. I dive into my logs and recent changes - usually my server hit resource limits. Basic troubleshooting, but it works! 3. Trust the Numbers!! I run my validation queries to check if things add up and verify key business rules. 4. Never patch without a rollback strategy and always test fixes in staging. Takes extra time but saves you from late-night emergencies. 5. Build monitoring alerts for all critical checkpoints. When something looks off, get notified before your stakeholders do. Major stress-saver! 6. Prioritize critical data loads, keep your team updated, and triple-check everything once it's done. No surprises = happy stakeholder!

1. Stop downstream processes and notify respective users. 2. Backtrack from the failure point, check if data discrepancy due to transformation logic or issue from source. 3. If issue from logic, keep validating data at each transformation point to check at which point the inconsistency occured. Work on fix and provide ETAs for data availability. If issue from source, contact source DB and notify about the issue. 4. Take backups and reprocess the data according to the job priority after the fixes are in place.

That is not easy to answer in short phrases as books were written about. This depends really on the kind of failure and the availability of the even wrong data. One possibility is to set up rollback mechanisms to come back to a consistent state of the data, look for the error and continue the process as quick as you can. You probably will loose a lot of time espacially whe process terminates a 2 am and everones sleeps. Other possibility is to write faulty data automatically into seperate error tables and continue with the remaining data. You can the check the errors, try to correct them and process the data afterwards. Take care that maybe you need to aggregate data once again. There are many more possibilities.. end of space for comment

Having worked extensively with ETLs and managing support teams, I’ve encountered many failures and data inconsistencies. Resolving them requires a structured approach: Identify the Failure – Logs are invaluable; learn to read them as they reveal the root cause. Corrective Action – Assess ETL setup. Can you restart, clean up, upsert, or skip faulty data? Recovery mechanisms are crucial. Revert to Original State – Undo temporary fixes; don’t leave them in production. Root Cause & Fix – Identify and resolve the issue at the source, ETL, or target. Update logic, add quality checks, test, and apply a permanent fix.

As an ETL QA, tackling ETL failure and data inconsistency involves several steps. First, identify the failure point by analyzing ETL logs to understand if the issue occurred during extraction, transformation, or loading. Next, validate data consistency by comparing row counts, running data integrity checks, and verifying transformations against business rules. Perform root cause analysis—check source availability, schema changes, and target constraints. Use backup data for recovery, and rerun the ETL after addressing issues. Implement automated validation scripts and set up monitoring alerts to prevent future failures. Finally, document findings to refine ETL processes.