Empromptu's "golden pipeline" approach tackles the last-mile data problem in agentic AI by integrating normalization directly into the application workflow — replacing weeks of manual data prep with ...

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 ...

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

This is where AI-augmented data quality engineering emerges. It shifts data quality from deterministic, Boolean checks to ...

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 ...

What if you could future-proof your career by stepping into one of the most in-demand tech roles of the decade? As companies increasingly rely on data to drive decisions, the role of a data engineer ...

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 ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. In this episode, Thomas Betts chats with ...

What’s the difference between a data engineer and a data analyst? Data isn’t much good without people who know how to collect it, shape it, and explain what it means. That’s where data engineers and ...

It’s always tempting to say that things were simple in the old days. But speak with any surviving COBOL or Fortran programmer, especially those who had to deal with punch cards or rotating drums, and ...