Bridging analytics, LLMs and data products in a single database 

The problem 

Data Mesh and data products were the biggest trends that organizations were exploring to modernize their data analytics space… until generative AI crashed the party. Now organizations need to deal with more than relational analytics data, such as vector embeddings produced by large language models (LLMs) from a diverse spectrum of “unstructured” (i.e., complex, non-relational) data.

You know what would be the winning solution?

A database system that can support tables and SQL, complex data and vector embeddings, while enabling the design of data products built on this diverse data and associated workloads.