Distributed database consistency models form the backbone of reliable and high-performance systems in today’s interconnected digital landscape. These models define the guarantees provided by a ...

Even as large language models have been making a splash with ChatGPT and its competitors, another incoming AI wave has been quietly emerging: large database models. Even as large language models have ...

AI initiatives don’t stall because models aren’t good enough, but because data architecture lags the requirements of agentic systems.

At its heart, data modeling is about understanding how data flows through a system. Just as a map can help us understand a city’s layout, data modeling can help us understand the complexities of a ...

Picture this: The year is 2030, and you’re living in a world where retailers can predict consumer behavior with uncanny accuracy, healthcare providers are diagnosing diseases before symptoms escalate ...

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, ...

While sensing technologies have advanced rapidly, the study identifies data fragmentation as one of the most persistent ...

A guide to the 10 most common data modeling mistakes Your email has been sent Data modeling is the process through which we represent information system objects or entities and the connections between ...

Data modeling is the procedure of crafting a visual representation of an entire information system or portions of it in order to convey connections between data points and structures. The objective is ...

Occasionally one may hear that a data model is “over-normalized,” but just what does that mean? Normalization is intended to analyze the functional dependencies across a set of data. The goal is to ...