Vector Databases: Taking Data Revolution to The Next Level
Here’s how AI-centric vector databases are gaining their place in modern software stacks.
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.
Vector Databases vs Traditional Databases
Relational databases store items with pre-defined relationships between them. These databases typically organize data in tables with columns and rows. This is known as structured data and is generally easy to search and analyze. In contrast, unstructured data is more complex…
