When databases are built from a well-designed data model, the resulting structures provide increased value to the organization. The value derived from the data model exhibits itself in the form of ...

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, ...

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

For a long time, companies have been using relational databases (DB) to manage data. However, with the increasing use of ...

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 ...

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.

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 ...

Oracle’s change in strategy to release a developer-first version of its new database is linked to the company’s plans to defend its market dominance and look at innovative ways to acquire new ...

Monte Carlo Data’s observability-centric updates, particularly support for popular vector databases, come at a time when enterprises are going all in on generative AI.

Telecom has more than 20 AI models in testing or production phase for customer management, marketing, and network ...