IEEE research highlights multi-model databases outperform single-model systems, reducing AI costs, latency, and schema issues ...

AI may be the visible goal, but data architecture is what determines whether that goal can actually be achieved.

Data foundations were never designed to support intelligent workloads at scale, but unified data lakehouse architecture might ...

Enterprise AI workloads require infrastructure designed for large-scale data processing and distributed computing.

By combining the efficiency of a Mixture-of-Experts architecture with the openness of an Apache 2.0 license, OpenAI is ...

A former Snowflake data scientist who refined multi-billion-dollar forecasts is now building AI models that outperform Claude ...

Data decay, dark funnel gaps, and identity issues limit visibility. Learn how to turn scattered signals into a connected, ...

In practice, retrieval is a system with its own failure modes, its own latency budget and its own quality requirements.

It's clear that AI agents are only as good as the data behind them. Now, Google Cloud databases are being rebuilt to feed ...

Scaling agentic AI demands a strong data foundation - 4 steps to take first ...

Don't let early success blind you; if your AI's behavior is getting harder to explain, it is time to stop patching the old ...

Oracle’s approach to bring AI to data (instead of the other way round) is being tested by SMRT, where safety demands ...