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 ...

Vector databases, on the other hand, store and process data in the form of vector embeddings, which convert text, documents, images, and other data into numerical representations that capture the ...

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.

To put it simply, if AI is the car that is driving the future of innovation, data is the energy source, and how we store that data matters.

Vector databases have built-in search capability that quickly delivers optimized and relevant results, especially with complex data sets such as image, video, and audio.

Redis is introducing a 'unified release' to make products easier for users to adopt and effectively use and manage their data.

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.

Machine learning is producing vast pools of scalar and vector data on a daily basis. With the introduction of more scalar data types, Milvus 2.1 is bridging this critical gap between data pools.