Semantic layer
A semantic layer gives each business metric one agreed definition, so people, BI tools and AI agents calculate it the same way.
Alex writes about how a semantic layer sits on top of a lakehouse and why agents give better answers with one.
Below are his articles, videos and podcast episodes that mention semantic layers, newest first, pulled from his feeds.
Articles
Turning an Analytics Question Into a Verified Agentic Graph
A complete AGS 1.0 graph for governed metric questions, with verification gates, deterministic check scripts, and reconciliation against a semantic la...
How Apache Ossie Is Deciding What Agents and BI Tools Can Ask a Semantic Layer
How Apache Ossie's layered query design gives AI agents both a constrained dimensional interface and a grain-safe SQL interface for semantic layers....
Agentic Data Architecture
A six-layer reference architecture for agents on company data: planners, tool boundaries, identity, the semantic layer, and what breaks when a layer i...
Apache Ossie and Apache Polaris: Putting Semantic Models in the Open Catalog
Apache Ossie and Polaris put metric definitions in the open catalog. What the spec covers, what Polaris stores, and what is still unfinished....
Semantic Layer Federation: One Logical Model Over Data on Three Clouds
One logical model over Iceberg and databases on three clouds. Pushdown, egress, Reflections, and where semantic federation still breaks....
The Five Layers of an Agentic Lakehouse
The five layers of an agentic lakehouse: Storage, Catalog, Semantic, Gateway, and Agent Surface, and how one question travels through all of them....
Semantic Layer Federation: One Meaning for Data That Lives Everywhere
Build a federated semantic layer across multi-cloud data so one set of governed metric definitions serves BI tools, dashboards, and AI agents identica...
The Five Layers of an Agentic Lakehouse and Where the MCP Server Sits
The five layers of an agentic lakehouse and where the MCP server sits: storage, catalog, semantic layer, MCP gateway, and agent surface, plus identity...
Why Agentic AI Needs a Governed Semantic Layer Behind the Model Context Protocol
Why agentic AI needs a governed semantic layer behind the Model Context Protocol: metric consistency, access control, Apache Ossie for portable....
Metric Contracts as the Interface AI Agents Actually Need
Metric contracts as the interface AI agents need: calculation, inclusion rules, grain, temporal semantics, ownership, semantic versioning, and testing...
Apache Polaris 1.7.0 and the Quiet Work of Making a Catalog Trustworthy
Apache Polaris 1.7.0 deep dive: idempotent writes, semantic models, stricter credential vending, orphan cleanup, and what the upgrade asks of you....
Why AI Agents Fail on Raw Data, and What to Give Them Instead
Agents fail on raw lake data because business rules live in people's heads. Data products with semantic contracts fix this at the source....
Governing What Agents Cost You
Agents break the four assumptions analytics platforms were built on. A practical guide to identity, budgets, semantic layers, caching, and instrumenta...
Semantic View Autopilot for AI Governance
An in-depth exploration of semantic view autopilot for ai governance...
The State of Apache Polaris in July 2026: From Incubating Catalog to the Governance Layer of the Open Lakehouse
Apache Polaris as a TLP, federation, credential vending, semantic layers, lineage, and how the open catalog became the governance plane....
The Metric Contract Mandate: Standardizing Semantic Layers Before AI Agent Access
AI agents are very good at moving quickly. That is the opportunity and the risk. If an agent can inspect metadata, generate queries, compare result......
The Who, What, and Why of Semantic Layers: The Layer That Decides Whether Your Numbers Can Be Trusted
There is a survey statistic making the rounds this year that I cannot stop quoting: 84 percent of data teams report regularly encountering conflict......
Composable Semantic Layers for Analytical Agents
AI agents need more than metric names. They need composable business logic that survives multi-step analysis....
The Context Layer for AI Agents
A semantic layer is necessary, but agents also need lineage, quality, freshness, compliance, and ownership context....
Lakehouse as the Operating Layer for Agentic AI
Agentic AI announcements are useful when they validate the need for governed data, semantic context, and cost-aware execution....
Fabric Agentic Analytics and Lakehouse Schema Design
Microsoft Fabric agentic analytics is a reminder that schemas, semantic models, and governed lakehouse design now shape AI behavior....
The Model Is Not the Moat
Enterprise AI advantage increasingly comes from governed context, semantic models, and operational data contracts, not only from model choice....
Anatomy of an Agentic Lakehouse
The four-layer architecture of the agentic lakehouse: object storage, Apache Iceberg table format, Apache Polaris catalog, and the semantic/agent laye...
Implementing MCP in the Lakehouse
How to build a Model Context Protocol (MCP) server that exposes lakehouse tables and semantic views as AI-accessible tools, with Python implementation...
Microsoft Fabric Build 2026 Agentic Analytics Stack
Microsoft Build 2026 revealed an agentic analytics stack built on Fabric IQ, OneLake Iceberg support, and semantic models....
SaaS Buyers Now Inspect Your Semantic Layer
Enterprise SaaS procurement in 2026 evaluates how platforms expose data to AI agents. Semantic layers have become a decision criterion alongside....
Semantic View Autopilot in Snowflake Semantic Studio
Snowflake Semantic View Autopilot automates semantic view creation from query history and BI assets....
The Semantic Layer as a Translation Engine: Bridging Natural Language and SQL
The semantic layer translates business language into accurate SQL for AI agents. Learn how virtual datasets, metric definitions, and wikis power agent...
Podcast episodes
Why do Semantic Layers matter so much?