In today’s AI gold rush, the startups that win aren’t just the ones with the best models—they’re the ones with the strongest data foundations. As AI-native companies race to productize intelligence, ...

Bloomberg’s Data Technologies Engineering team is responsible for the data collection systems that onboard all of the referential data that drive the company’s applications and enterprise solutions.

I believe the only reliable way to achieve goals as a product leader is through enabling the teams that we have the privilege to lead. In part one of this three-part series, I went over how product ...

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 ...

The latest trends in software development from the Computer Weekly Application Developer Network. This is a guest post for the Computer Weeklyu Developer Network written by Jesse Anderson in his role ...

Data engineering is the process that makes it usable. It involves moving, cleaning, and organizing. This creates the foundation for BI and analytics. The goal is to replace guesswork with facts. That ...

Data engineering is the gritty, often unglamorous work that underpins every AI model, every dashboard, and every strategic data driven decision. For years, we treated our data lakes like giant, messy ...

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 ...

The latest trends in software development from the Computer Weekly Application Developer Network. This is a guest post for the Computer Weekly Developer Network written by James Sturrock, in his ...