Forum Discussion
Mirrored Databricks Catalog - Semantic Models Don't Receive Schema Updates
So it seems like turning off the automatic sync helped.
We've not had issues since then. Following up on the audit log of databricks (query table system.access.audit ) also shows a lot fewer events for this user.Manual refreshes of any model still seem to be very intensive on API-calls, so when working with multiple developers, that's definitely something to keep in mind.
To turn off the automatic sync for the default model, you need to go to the settings of the SQL Analytics Endpoint, then default Semantic Model.
Hi sdmj
Custom semantic models do not automatically receive schema updates from the Mirrored Databricks Catalog. Manual refreshes are required, and repeated or concurrent refreshes can trigger Databricks API rate limits, resulting in REQUEST_LIMIT_EXCEEDED errors. To avoid this, monitor and coordinate API usage, use batching and backoff strategies, and consider automating updates with Databricks Workflows
Wait before retrying. The rate limit is usually reset after a short period (often a minute or so, but this can vary).
• Avoid repeatedly refreshing the semantic model in quick succession.
Even with a small model, certain operations—such as schema refreshes, credential validations, or repeated attempts to update the model—can collectively trigger rate limits, especially if multiple users or automated processes are accessing Databricks resources at the same time.