Dave is the founder of AtScale and is the Chief Technology Officer. Prior to AtScale, he was VP of Engineering at Klout & at Yahoo! where he built the world’s largest multi-dimensional cube for BI on Hadoop. Mariani is a Big Data visionary & serial entrepreneur.
May 22, 2026
10 Things We Learned at the 2026 Semantic Layer SummitMost enterprise AI governance conversations start with LLMs. But when an AI agent returns a wrong answer, the failure usually traces back to a much simpler problem: the business logic behind the answer was never defined in a place the…
May 4, 2026
Google Just Made Iceberg the Format Foundation of Compute. Make Sure Your Semantics Are Ready to Travel.What Google announced at Cloud Next wasn't an upgrade. It was a starting gun. Google announced a cross-cloud lakehouse standardized on Apache Iceberg. Not BigQuery's tables. Not a Google format. Iceberg. Open. Sitting in object storage you own. Readable by…
April 30, 2026
BI Tools Keep Trying to Own Your Semantic LayerEvery decade, someone reinvents the BI-embedded model. Every decade, customers end up migrating off it. The semantic layer is the context layer for AI, and putting it inside one BI tool was the original sin. Last week, Omni raised a…
April 27, 2026
3 Takeaways from Google Cloud Next 2026Google Cloud Next 2026 wrapped up this week at Mandalay Bay, and I’ve got thoughts. Some of them are even about the announcements. But let’s start with the elephant, or rather, the 40,000 people crammed into a venue built for…
April 24, 2026
The Sensing Factory Needs Context: Why Semantic Infrastructure Is Critical for Agentic AI in ManufacturingExecutive Summary (The TL;DR) The Bottom Line: Agentic AI in manufacturing promises autonomous optimization, but without a Semantic Layer, it functions as a "high-speed hallucination engine." For a Tier 1 manufacturer, the lack of governed context results in an average…
April 16, 2026
Why Retail AI Agents Need Business Context for Autonomous CommerceRetailers that deploy autonomous AI without governed business context will not discover the problem in a report. They will discover it in a fulfillment failure, a margin error, or a pricing decision that made sense to the agent but not…
April 9, 2026
From Data Gravity to Context Gravity: Why AI Is Reshaping Data ArchitectureAI is shifting the center of gravity in data architecture from raw data to a governed business context. The last decade of data architecture was shaped by data gravity. The next decade will be shaped by context gravity. The cloud…
April 1, 2026
Not All Semantic Layers Are Built for AIThe semantic layer has moved from niche infrastructure to a boardroom priority. Snowflake announced one. Databricks announced one. Google repositioned Looker as one. Microsoft repositioned Power BI as one. This is genuinely good news. As ISG analyst Matt Aslett observed…
March 26, 2026
Power BI & Excel on Snowflake Without Data Movement | Semantic Layer for AIMost enterprises have already standardized where their data lives. What they haven’t standardized is how that data is interpreted. Power BI and Excel users still rely on a workaround: moving data out of Snowflake into extracts, cubes, or Microsoft Fabric…
March 17, 2026
Why Moving Data for BI and AI Creates Architecture ProblemsIn modern data architectures, analytics tools often replicate data from the warehouse into secondary systems to improve performance. While this can speed up dashboards, it introduces new problems: duplicated datasets, inconsistent metrics, fragmented governance, and rising cloud costs. Modern analytics…
March 12, 2026
Hits and Misses from Gartner Data & Analytics Summit 2026Gartner opened the Data & Analytics Summit 2026 with a memorable metaphor. The keynote framed the current moment as a raft ride through a roaring, treacherous river. The river represented AI. Fast-moving. Powerful. Difficult to control. The message was clear.…
March 11, 2026
AI Can’t Run on Untrusted Data: Why Agentic Analytics Requires Live Semantic ConnectivityA logistics company deploys an AI agent to monitor inventory and automatically trigger reorders. The model is solid. The prompts are well-engineered. The agent connects to their analytics environment and starts running. Three weeks in, it fires a reorder for…