I listened to Data engineering podcast -‘real time vector search’

The interviewee is a vendor, but nevertheless the talk about vector databases was interesting and not a pitch for rocksetdb.

They talked about vectors, as they are used in ML (machine learning), for instance in embeddings and large language models. Vectors are a numerical representation of something, with the idea that they live in a more semantic space. points in that space that are closer together are semantically more similar. But technically it is a an fixed size array for every row in your table.

Some ideas I wanted to highlight:

- At some level many machine learning solutions are vector search problems (or you can turn them into one): finding the nearest neighbor, recommendations.
- You can translate many types unstructured data into a vector space
- describing and translating a problem (into vector space problems) is a core part of data science solutions.
- You probably don’t need a special database for vector search, see it as just another column (with special properties) in your table.
- vector data and vector search work better for OLAP (online analytical processing) databases.
- managing the lifecycle of vectors, (adding new ones, updating, deleting) is a data engineering problem; you’re maintaining a data base or data warehouse.

*I’m publishing this as part of 100 Days To Offload. You can join in yourself by visiting https://100daystooffload.com, post - 16/100*

*Find other posts tagged #100DaysToOffload here*