There is no universal ‘best’ vector database—the choice depends on your needs. Evaluating scalability, functionality, performance, and compatibility with your use cases is vital.

A vector database becomes essential when handling unstructured data requiring complex algorithmic work, such as high-dimensional data, similarity searches, real-time AI applications, or when ...

Vector databases and search aren’t new, ... and my job was to convert each DNA pattern’s image into representable data. ... manage, and query different semi-structured and unstructured datasets.

With Redis Data Integration (RDI) in the Redis 7.2 update, the company is providing an integrated approach to help get data from other data sources including Oracle Database, PostgreSQL, MySQL and ...

Although vector databases have been around for decades, their application was limited. Forrester estimates the current adoption rate of vector databases at 6%, with a projected surge to 18% over ...

Milvus, a Linux Foundation AI and data project, for example, is a well-known vector database of choice among enterprises that’s easy to try out because of its vibrant open source development.

Since the turn of the year, we’ve also seen Index Ventures lead a $9.5 million seed round into Superlinked, a platform that transforms complex data into vector embeddings.

MariaDB has recently released MariaDB Community Server 11.8 as generally available, its yearly long-term support (LTS) ...