Silent schema drift is a common source of failure. When fields change meaning without traceability, explanations become ...

The evolution of artificial intelligence, data engineering, and enterprise systems is no longer driven by isolated breakthroughs. It is shaped by practitioners who build, scale, and govern technology ...

Today’s data landscape presents unprecedented challenges for organisations, due to the need for businesses to process thousands of documents in numerous data formats. These, as Bogdan Raduta, head of ...

Mukul Garg is the Head of Support Engineering at PubNub, which powers apps for virtual work, play, learning and health. In my journey through data engineering, one of the most remarkable shifts I’ve ...

Though the AI era conjures a futuristic, tech-advanced image of the present, AI fundamentally depends on the same data standards that have been around forever. These data standards—such as being clean ...

While some consider prompting is a manual hack, context Engineering is a scalable discipline. Learn how to build AI systems that manage their own information flow using MCP and context caching.

AI Engineering focuses on building intelligent systems, while Data Science focuses on insights and predictionsBoth careers offer high salaries and ...

KDNuggets, a community site for data professionals, ranked “We Don’t Need Data Scientists, We Need Data Engineers,” by Mihail Eric, a venture capitalist, researcher, and educator, as its top story of ...

Data centers and high-performance computing (HPC) are the primary enablers of today’s power-hungry AI-driven technology, but chip designers, EDA vendors, and the data centers themselves have a long ...