Updated September 29, 2026
Microsoft’s About-Face on Semantic Layer StandardsWhen Snowflake launched the Open Semantic Interchange in September 2025, dozens of companies signed on. Microsoft wasn't one of them. Power BI's semantic models stayed in DAX, built for Power BI, while the rest of the industry worked on a…
Updated August 20, 2026
The Multi-Cloud Metrics Problem Snowflake, Databricks, and BigQuery Can’t Solve AloneEnterprises running Snowflake, Databricks, and BigQuery side by side no longer struggle with data movement. Data pipeline tools solved that years ago. Now they have a metrics problem: the same "gross margin" or "active users" number means something different depending…
Updated July 22, 2026
My Case for the Semantic Layer, Made Live on Atlan’s PodcastI recently joined Austin Kronz on Atlan's podcast, WTF is the Context Layer, to discuss one of the biggest sources of confusion in enterprise AI: the distinction between semantic layers, context layers, and ontologies. We covered a lot of ground,…
Updated August 17, 2026
Snowflake Semantic Views vs. Universal Semantic Layer: Which One Does Your Enterprise Actually Need?Snowflake Semantic Views let you define metrics, dimensions, and relationships directly inside Snowflake, with no extra tooling required. A universal semantic layer sits above your warehouse and works across Snowflake, other data platforms, and every BI tool you run on…
Updated July 9, 2026
Three Companies Solved Their AI Problem Before AI Was the ProblemAn agent is only as reliable as the definitions it's built on. And every company racing to deploy agentic AI right now is about to learn it the hard way. Here's what's actually happening at TELUS, Vodafone, and Slickdeals, three…
Updated July 7, 2026
Why I Joined AtScale: The Semantic Layer Inflection PointOver four and a half years at Snowflake, I watched enterprises run into a problem that's about to get much worse: every major platform has built its own semantic layer, and none of them talk to each other. Many platforms…
Updated July 1, 2026
Enterprise AI: Separate the Reasoning from the CalculationAt this year’s Semantic Layer Summit, practitioners from Anthropic, Chevron, WPP, Accenture, NVIDIA, and OpenHands, compared notes on why production AI keeps breaking down. The consensus? The LLM is not the bottleneck. It’s context. The organizations succeeding in production don’t…
Updated June 25, 2026
The Semantic Layer’s New JobEighteen months ago, Blue Yonder’s analytics engineering team made a decision most data organizations haven’t made yet: they stopped being a BI team. They saw what was coming. AI agents don’t just need access to data. They need to understand…
Updated June 17, 2026
It’s Got to Be the Semantic Layer, BabyAtScale's Dave Mariani and Snowflake's Carl Perry on why governed semantics, not a smarter LLM, is what makes AI agents trustworthy. For years, business intelligence ran on a human workaround. When two reports disagreed about "revenue," a trained BI analyst…
Updated June 11, 2026
Power BI Was Built as a Client. Stop Using It as a Foundation.Microsoft wants to own your semantics. We built the universal layer that keeps them yours. Power BI is a strong BI client. It was never an enterprise semantic layer, and the gap between those two jobs is now showing up…
Updated June 9, 2026
A Tech Power Player in the Year the Semantic Layer Broke ThroughChris Lynch has been saying the same thing for years. The market just caught up. For years, I’ve been a board member of AtScale, and Chris Lynch said the same thing with each meeting: AI is changing everything for semantic…
Updated June 4, 2026
Why Semantics, Not Metadata, Are the Key to Business Context for AIFive queries. $17.93 with an unguided LLM. Less than a tenth of a cent with a semantic layer. That 21,000x cost gap, measured by the commercial banking division of a Tier 1 multinational bank, is what happens when AI has…
