Accelerating SQL and BI Analytics (White Paper)

The shift toward real-time decision-making at the edge is an evolution in how businesses operate. Closing the latency gap means smarter, safer, and more resilient operations.

AI agents represent an opportunity to go beyond isolated analytics projects and move toward self-improving, autonomous workflows that directly drive operational performance.

Scaling AI in industry requires the right technology, a practical understanding of industrial operations, and open interoperability with an organization’s existing data infrastructure.

The enterprise data stack is at an inflection point. Storing and streaming data is no longer enough. Businesses need a combination of streaming technologies, high-performance in-memory databases, and adaptive intelligence to support real-time enterprise operations.

The ability to deliver AI-ready, trusted data depends less on new tools and more on how effectively DataOps are automated, governed, and observed.

With the right automation framework in place, infrastructure can react as conditions change, without human intervention.

Dashboards helped us see our business, but they won’t run the future of robotic, algorithmic, sensor-powered enterprises now emerging. The companies that embrace this shift early will accelerate.

In this week’s real-time analytics news: Sovereign Cloud efforts in the EU are heating up. SUSE launched a Cloud Sovereignty Framework Self Assessment to help businesses understand gaps in their digital strategy.