Knowledge Graphs vs. Vector DBs: Similarities and Differences To help understand both the technology and the business impact, it’s important to understand what each of them do.

Vector databases unlock the insights buried in complex data including documents, videos, images, audio files, workflows, and system-generated alerts. Here’s how.

We look at the use of vector data in AI, how vector databases work, plus vector embedding, the challenges for storage of vector data and the key suppliers of vector database products ...

By reducing the need to structure data, vector databases can speed up generative AI training times to dramatically improve our productivity.

Vector databases are designed for efficient storage, retrieval and similarity search of vector data and are a key building block for generative AI systems.

In an enterprise application, this hybrid capability of storing both vectors and tabular data provides developers with a flexibility that is not available in pure vector databases.

How Vector Search Works Vector search begins by transforming data into mathematical vectors—dense, numerical arrays that represent the meaning or context of the content. For example, a sentence ...

Learn More Data platform vendor DataStax is entering the vector database space, announcing the general availability of vector search in its flagship Astra DB cloud database.