You're faced with data discrepancies during a system upgrade. How will you resolve them effectively?

- Obtain logs from the upgrade process, including error logs, system performance logs, and any reports generated during the upgrade - Verify if the data post-upgrade matches expectations. Look for inconsistencies, missing records, or incorrect values - Use data snapshots taken before and after the upgrade to identify discrepancies. Compare key data sets, focusing on critical fields and records - Verify that security features and access controls are intact and functioning properly

First and foremost is to actually identify the issues that have popped up! If its possible that while system is being upgraded, you can monitor LIVE all the changes in the new data, do that! If not, Always try to upgrade the system if its in UAT environment or on a smaller service

Identify, find the root cause, rerun the reports and compare with data that is confirmed accurate as many times as necessary, consult with your leaders, take corrective action, grow and improve.

Identify the source of discrepancies: Start by identifying the specific data elements or records that are showing discrepancies. Determine whether the discrepancies are due to data migration issues, system configuration errors, data quality issues, or other factors.

- Decide how often backups should be taken (e.g., daily, weekly). For upgrades, you should aim to have backups immediately before the upgrade begins - Use backup software or services that automate the backup process. This reduces the risk of human error and ensures consistency - Use physical storage solutions (like external drives or network-attached storage) for immediate access to backups. Ensure these are stored securely and are easily accessible - Confirm that the backup has completed successfully and that no errors occurred during the backup process - Document the backup procedures, including schedules, tools used, and storage locations

- Establish what constitutes clean and valid data for your specific needs. This includes accuracy, completeness, consistency, and timeliness - Use data profiling tools to analyze data sets and identify anomalies, patterns, and inconsistencies - Define what you aim to achieve with data cleaning - ix errors such as typos, incorrect values, or outdated information. This may involve cross-referencing with reliable sources - Check data against known rules or constraints (e.g., valid ranges, formats) to ensure it adheres to expected values - Document the data cleaning procedures, rules applied, and any transformations performed - After cleaning, review the process and outcomes to identify areas for improvement

Testing is crucial to validate successful resolutions. Create comprehensive test cases covering all resolved discrepancies and execute them in a production-like environment to catch unforeseen issues. Prioritize performance and scalability to ensure corrected data doesn't affect system operations negatively.

After resolving discrepancies and deploying changes, closely monitor the system for new issues. Use alerts or reports in your DBMS to detect potential problems early. This ongoing vigilance ensures database integrity post-upgrade.