Differing data versions typically have one of two reasons for the differences: a time/date difference or an intermediate system that has performed some operation against the data, sanctioned or not. The best way to solve both problems is to define a source of record for the data, and for all downstream systems to reference that source and that source only. Each data access or refresh should also have an associated "time to live" (TTL) or better, a timestamp defining a (source of record) window of time in which the data is considered valid, and beyond which will need to be refreshed. This may not resolve every incident, but it will go a long way towards that, and any remaining instances can be addressed as the outliers they most likely are.

What Donald Worthington and Marty Schrader said applies to almost every situation: define a "source of truth" for each kind of datum, control very strictly what processes can write to a given database, ensure each process respects the source of truth, and use timestamps/version numbers metadata to record what version is being written. It may not always be possible to contact the source of truth to check whether a datum is stale, e.g., because there is a network outage. Here, there are two choices. 1/ Ask the user or client application to retry later. 2/ Write the datum with additional metadata, and use that later to "merge" the different copies of the data automatically or ask for user intervention. The Riak DB is an example of such a DB.

Dealing with conflicting data versions across distributed databases is like untangling a knot—you’ve got to work methodically. I typically start by implementing a versioning system or timestamps to track the "source of truth" for each piece of data. In one project, we faced this issue when nodes in different regions started updating out of sync. We used a conflict resolution strategy that prioritized the most recent, authoritative changes, combined with eventual consistency models to sync databases over time. Sometimes, though, it’s about picking your battles—deciding which conflicts can be automatically resolved and which need manual oversight to avoid data drift.