Many companies are choosing to build their own AI systems, rather than buy. Financial services firm Kapitus shares lessons learned.

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.

Key data and analytics trends in 2026 include decision intelligence, real-time analytics, semantic layers, platform ...

"These statistical agencies are essential infrastructure," Nancy Potok said. "There are so many critical decisions made based ...

The world tried to kill Andy off but he had to stay alive to to talk about what happened with databases in 2025.

As for the AI bubble, it is coming up for conversation because it is now having a material effect on the economy at large.

Abstract: Cloud-based data pipelines are critical for large-scale ETL and big data analytics, yet in-efficient scheduling leads to high costs and resource underutilization. Traditional approaches, ...

Learn about extract, transform, load, including the benefits, drawbacks, and top tools, in this comprehensive guide. Databricks, AWS and Google Cloud are among the top ETL tools for seamless data ...

As the volume, velocity, and variety of data continue to accelerate, developers are facing a critical shift: data is no longer just stored and queried--it's constantly on the move. From traditional ...

Electricity rates for individuals and small businesses could rise sharply as Amazon, Google, Microsoft and other technology companies build data centers and expand into the energy business. A ...