Today at Microsoft Ignite, Microsoft announced SQL Server 2025. The biggest new features focus on AI and bringing the latest Azure SQL DB features down to your own servers. Here are the top features:
- Call AI services like ChatGPT directly from T-SQL – using T-SQL commands and sp_invoke_external_rest_endpoint
- Vector searches, a native vector data type, and vector indexes with DiskANN – more information in the documentation
- Fabric mirroring – so your data warehouse in Fabric can more easily have a near-real-time copy of your OLTP data sources – similar to how it works in Azure SQL DB
- Regular expression support in T-SQL – to learn more about that, read how it works in Azure
- Optimized locking – like how it works in Azure SQL DB, and you might have noticed a theme here
Of this list, I’m excited for regular expression support and optimized locking. On the latter, I would like RCSI to be the default, but I can understand that this would have a significant “Who moved my cheese?” factor for DBAs, and there are just enough cases when RCSI introduces actual change into an environment.
Fabric mirroring is also fine, and sp_invoke_external_rest_endpoint is something that can be useful, not just for making external calls. As for storing vector data, I will want to see its performance—both in terms of how quickly it loads & retrieves data, as well as how accurate the results are—before I get too excited. DiskANN is a good algorithm and it’s in Cosmos DB right now, but I haven’t been that impressed with the quality of results so far from it in Cosmos.
How about Vector in MongoDB?
I’m aware of vector database support in Mongo but haven’t done much with it. It does seem to be one of the bigger platforms for non-specialized vector storage. Not sure how it stacks up to products like chromadb, though.