1. Check ADF portal for failed/pending activities 2. Verify data source availability 3. Monitor runtime metrics in Log Analytics

1. Query source database connection status 2. Check storage account accessibility 3. Review Data Factory execution logs in Kusto 4. Validate copy activity parallelization settings

1. Increase DPU allocation to ADF IR if CPU throttled 2. Retry failed pipeline with increased timeout 3. If data source slow: optimize source queries 4. Restart Integration Runtime if unresponsive 5. Monitor next execution for stability

1. Revert DPU allocation to baseline 2. Document performance metrics 3. Run validation queries to ensure data consistency 4. Resume normal monitoring

1. Monitor SQL Agent job history 2. Check error code in SSISDB catalog 3. Verify memory pressure on execution server

1. Query SSISDB for execution logs and warnings 2. Check SQL Server memory and CPU 3. Validate table locks in claims database 4. Review external API response logs

1. Clear SSIS object cache if aged 2. Optimize transformation script buffer sizes 3. If database locked: identify blocking session and kill if safe 4. Restart SQL Server Agent job 5. Execute full validation on sample 10K records

1. Verify rollback integrity with spot checks 2. Run validation queries on standardized fields 3. Monitor downstream dependent jobs 4. Alert data consumers if needed

1. Check Databricks cluster state in console 2. Monitor job run in Workflows UI 3. Check Spark driver/executor logs

1. Analyze query execution plan in Databricks SQL 2. Check cluster auto-scaling metrics 3. Review data file sizes being scanned 4. Monitor network throughput to data lake

1. Scale cluster from 4 to 8 workers 2. Increase Spark shuffle partitions from 200 to 500 3. Optimize SQL query with column selection 4. Add data filter predicate if applicable 5. Retry workflow job

1. Scale cluster back to 4 workers 2. Reset Spark config to defaults 3. Verify data consistency with checksums 4. Document performance baseline

1. Check scheduled task execution log 2. Review stderr output from last run 3. Check process CPU and memory usage

1. Run script in debug mode with verbose logging 2. Validate all external API endpoints 3. Check Python environment (pip list) 4. Verify database connection string validity 5. Check disk space on execution server

1. Update pip packages (pip install --upgrade) 2. Fix timeout values for slow endpoints 3. Add retry logic with exponential backoff 4. Validate all environment variables 5. Test locally before re-enabling schedule

1. Revert pip packages to previous versions 2. Restore previous script version from git 3. Run validation against test dataset 4. Restore monitoring alerts

1. Check Fortra job status in their portal 2. Monitor local staging directory for files 3. Check FTP/SFTP connection logs

1. Verify Fortra API authentication and rate limits 2. Check network connectivity to Fortra servers 3. Monitor disk space on staging area 4. Review firewall rules for Fortra IP ranges

1. Contact Fortra support to check remote job status 2. Trigger manual re-execution if safe 3. Clear stuck locks on staging files 4. Restore network connectivity if needed 5. Resume polling for file arrival

1. Validate file checksums if re-transferred 2. Replay missing data through transformation 3. Monitor for duplicate processing 4. Clear staging area of failed attempts