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.

Washington and Big Tech signed a pledge but missed an opportunity to lower the tab.

The latest MLPerf benchmark results paint a clear picture of an AI infrastructure ecosystem in rapid transition.

Stop building your RAG pipelines to process what your models already know. Let the models capture the knowledge itself.

As the pace and complexity of operations accelerate, organizations are rethinking architectures to enable real-time, human-in-the-loop decision-making.