Behind AI #3: Vector Databases - Taking Data Revolution to The Next Level

Here's how AI-centric vector databases are gaining their place in modern software stacks.

In the previous article of our Behind AI series, we explained in plain English what databases are and compared the top DBMS. Today, we’ll focus on a type of database that has become increasingly important in AI: vector databases.

The internet contains a huge amount of data in different forms. In the past, this data was mostly structured, but as the internet grew, unstructured data such as photos, audio, text, and video files became more common.

Analysts estimate that 80-90% of any organization’s data is unstructured, so how can we deal with this unstructured data?

Traditional relational databases and NoSQL databases struggle to analyze unstructured data especially when it comes to doing it in real-time. Here’s when vector databases can help. They were built to manage massive embeddings vectors converted from unstructured data.

In this article, we’ll learn more about vector databases, how they can help us manage unstructured data, and the solutions they offer.