Data modeling is the framework that lets data analysis use data for decision-making. A combined approach is needed to maximize data insights.

Data models are used to represent real-world entities, but often have limitations. Avoid common data modeling mistakes for data integrity.

Enterprises are creating huge amounts of data and it is being generated, stored, accessed, and analyzed everywhere – in core datacenters, in the cloud distributed among various providers, at the edge, ...

Looking Ahead: The Potential Of AI Databases As we move into an increasingly data-driven future, the role of AI databases will only continue to grow in importance.

This approach features a centralized database linked to other data stores with a common data model that carries information from one point to another, without the need to rewrite code.

Document databases have long been the backbone of critical applications — now, they’re entering a new era of intelligence and capability. By embedding generative AI directly into the data ...

According to news from the organizing committee of the 2025 Data Expo, domestic database leader Electric Science and ...

The process of de-identifying test databases can be approached in a variety of ways, and we’re often asked how our approach differs as compared to others. In this article, we’ll explore how our ...

LlamaIndex, a startup creating a framework that allows users to add personal data to large language models, is attracting VC backing.