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Data Hydration covers the activities that support AI models being supplied with an adequate supply of essential information to allow them to thrive.

As enterprises deploy AI agents across procurement, customer service, and financial operations, the observability gap is becoming a governance crisis.

Data immediacy goes beyond the concepts of streaming data and data-in-motion. It adds the dimension that data has a very specific time value. The less recent the data is, the less valuable it is. Here are the issues you should consider to implement data immediacy and realize its benefits.

Tomer Shiran, Co-Founder of Dremio, provides an insider’s perspective about Apache Iceberg, its emergence as a standard table format, and how AI is optimizing query performance.

Explore the top smart manufacturing trends for 2026, including AI, IoT, and automation. Learn how manufacturers are using these technologies to reduce costs, improve efficiency, and build resilient, data-driven operations.

Modern enterprise applications, especially those driven by real-time data, AI inference, and latency-sensitive user experiences, are exposing the limitations of a one-size-fits-all cloud strategy, forcing organizations to rethink workload placement across hybrid and distributed environments. The shift is no longer about “cloud vs. on-prem,” but about architecting for performance, cost predictability, and data gravity in a world where systems must operate continuously and adapt dynamically.

The AI revolution isn’t happening in GPUs or model architectures. It’s happening quietly in the data pipelines that feed them.

The future of AI in the enterprise is recognizing that intelligence, whether human or artificial, must be governed by clear, written code.