Unstructured data is messy, scattered, and multimodal — making it hard to manage and essential to control for successful enterprise AI adoption.

We look at how to gain structure from unstructured data, via AI/ML analytics to create new records, selecting object data via SQL and storing unstructured files in NoSQL formats.

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.

VCs are hungry to back vector database startups and other behind-the-scenes tech that improves AI. Vector databases store and structure data that LLMs can then pull from. Business Insider has ...

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 ...

Unlike structured data, which fits neatly into rows and columns in a database, unstructured data is usually in its raw form, often comprising text, images, audio, or video.

Data Cloud Vector Database will unify all business data, including unstructured data like PDFs, emails, and transcripts, with CRM data to enable grounding of AI prompts and Einstein Copilot ...

This is how modern AI databases work with the types of unstructured data mentioned above. Instead of looking for exact matches, these databases look for results that are similar or “close enough.” ...

An SQL database might hold name, date of birth, address, etc, but analysing unstructured data – via making it semi-structured – can get closer to what consumers think.

TL;DR Key Takeaways : Vector databases store and retrieve unstructured data (e.g., images, text, audio) using vector embeddings, allowing semantic search and bridging the “semantic gap ...