Reconciliation using DB2 Journal Entries in Streaming Ingestion Architecture: A Comprehensive Guide

When it comes to reconciling data between DB2 on IBM i and Azure SQL Hyperscale for streaming ingestion, performance is a key concern. One popular method to achieve this without querying the live DB2 system itself is by using DB2 journal entries.

But how reliable is this method for row-level reconciliation? Well, it turns out scanning journal receivers for committed data modifications is indeed a supported and dependable approach. Here are some key considerations and best practices to keep in mind:

Firstly, make sure that journaling is enabled on the source tables and that only committed changes are being captured. Using commit timestamp windows like JRN_TIMESTAMP can help align changes against CDC payloads in Azure SQL. And maintaining a persistent offset or sequence number will allow you to easily resume scans.

In terms of performance and scalability, journal access is typically lighter than querying live tables, but you’ll want to monitor the latency of journal availability for near real-time ingestion. And it’s best to avoid deep historical scans and only extract entries since the last checkpoint.

Overall, using DB2 journal entries for reconciliation is a stable approach, especially since many enterprise CDC tools rely on these journaling APIs internally. Just make sure to handle them deterministically and consider implementing control tables and idempotent merge logic downstream for a seamless process.

I hope this information proves helpful for your data reconciliation needs. Feel free to reach out if you have any more questions or concerns. And if you found this information useful, consider giving it an upvote to help others in the community dealing with similar issues.