@KaranG62

You can handle this by running a daily Job Scheduler that reads the records from the DB table in small chunks (for example 500–1000 rows at a time) instead of loading all 100K at once. Use a report definition or database view to fetch only the keys you need (like case ID and the value to update), ideally filtered by date or a “pending” flag. For each row, send the case ID to a Queue Processor so the actual case update happens asynchronously in the background. In the Queue Processor activity, open the case, update the required property, save, and commit the change. Mark each processed row in the DB table with a flag or timestamp so the next run doesn’t pick it again. Enable parallel processing on the Queue Processor (multiple threads/partitions) so large volumes are handled within the batch window. Add proper logging and an error flow so failed items are captured and can be requeued. Test this with a smaller data set first to tune the batch size and schedule before running it on the full 100K+ records.