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.

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 ...

Unstructured data is stored in its native format, which means it lacks a pre-defined data model or schema and cannot be managed in a traditional relational database.

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.

Milvus is one of the world’s most advanced vector databases, capable of managing massive quantities of both structured and unstructured data to accelerate the development of next-generation data ...

In the RAG use case unstructured data is ingested into vector databases for retrieval. The metadata can be used to filter, rank and curate data used in RAG, ensuring the quality of the information ...

Unstructured data can’t be easily stored in a traditional column-row database or spreadsheet like a Microsoft Excel table. It’s therefore more difficult to analyse and not easily searchable ...

Vector databases are ideal for applications where the goal is to find data points similar to a given vector. For example, a vector database can find images similar to a given image, or text ...

Some data sources lack predictable order, but this doesn’t mean they aren't useful. Some AI scientists specialize in making sense of this unstructured data.

Because AI workloads are powered mostly by unstructured data, companies need visibility into their vector database stores and the data behind each prompt, the analyst said. Simply monitoring data ...