Snowflake’s new products and partnerships will better support organizations’ data management and AI agent implementation initiatives, especially when it comes to governance. In a recent interview with ...

IRVINE, Calif.--(BUSINESS WIRE)--Cotality™, a leading global property information, analytics, and data-enabled solutions provider, today announced at Snowflake’s annual user conference, Snowflake ...

Matt is an associate editorial director and award-winning content creation leader. He is a regular contributor to the CDW Tech Magazines and frequently writes about data analytics, software, storage ...

AtScale, the leader in Universal Semantic Layer technology, today announced at Snowflake’s annual user conference, Snowflake Summit 26, Snowflake Semantic Views XMLA Endpoint, powered by AtScale; a ...

SAN FRANCISCO, June 02, 2026 (GLOBE NEWSWIRE) -- ThoughtSpot, the Agentic Analytics Platform company, today announced at Snowflake’s annual user conference,Snowflake Summit 26, integrations of Spotter ...

SAN FRANCISCO, June 02, 2026 (GLOBE NEWSWIRE) -- RelationalAI, a leader in enterprise AI, today announced at Snowflake's annual user conference, Snowflake Summit 26, a series of new capabilities for ...

Enterprise AI agents have a new production failure mode, and it is not the model. As enterprises move from single-layer RAG to hybrid retrieval architectures, the same underlying data produces ...

The next phase of enterprise AI will be decided less by models and more by how enterprises govern, secure, and operationalize AI across fragmented environments. Most enterprises already have access to ...

New AtScale offering helps bring governed Snowflake metrics to Microsoft analytics tools, delivering consistent answers across AI, dashboards, and spreadsheets without duplicating data or recreating ...

Alation announced that its Data Products Marketplace now includes Semantic Model Mastering, a new capability that lets enterprises catalog semantic models from any platform, govern them as data ...

As enterprises move from AI experimentation to production deployments, one challenge is becoming increasingly apparent: AI systems are only as reliable as the business context they operate in.