Why It’s Important
Google has added vector support to most of its enterprise database platforms. Microsoft’s announcement of its PostgreSQL extensions confirms IBRS’s prediction that vector databases (those that support LLM embedding) will be merged with mainstream databases. We also expect that new, emergent AI database technology, such as Graphs, will head in the same direction.
The immediate impact of these announcements is that leveraging AI within PostgreSQL environments will greatly enhance efficiency in data management tasks. The embedding generation within the database and Copilot features facilitate greater ease of use and improve interaction with data, thus enhancing the productivity of development teams. Additionally, compliance with confidential workloads ensures that enterprises can safely integrate these capabilities without compromising security.
In the longer term, bringing vectors and potentially graph structures into familiar databases will herald as much change in the database landscape as when SQL was adopted as the standard in 1986. The opportunities to treat data not just as a searchable asset between structured and semi-structured models but also as an AI network opens up many new applications.
Adding AI helper agents over the top of databases will also quickly lead to even greater ‘self-managing’ database features.
Who’s Impacted?
- Database administrators
- Application developers
- Data scientists
What’s Next?
- Prepare the database teams for the coming advances in AI-infused databases. They will need to become versed in not only traditional database concepts, but learn how to manage and tune vector and eventually graph data.
- Explore the Copilot capabilities now available.
- Review current database workflows and identify areas where AI integration could improve efficiency.
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