Semantic Layer8 min read
Ossie vs OKF: Semantic Model or Markdown Context?
Apache Ossie and Google's Open Knowledge Format both want agents to run metrics exactly as defined. What each one does, where each fits, and why you need both.
The Semantic Layer: Your Company's Universal Language
A semantic layer translates raw warehouse tables into business concepts: metrics, dimensions, and hierarchies that every tool and every AI agent can rely on. It's the difference between data that's stored and data that's understood.
Semantic Model
metric:net_revenue
sumUSD
Governed·last verified 2024-Q4
description:
Total revenue, net of refunds, discounts, and chargebacks. Source of truth for board reporting.
expression:
SUM(orders.amount)- SUM(refunds.amount)- SUM(discounts.amount)
entity:
order
grain:
day
dimensions:
product_categoryregionchannelperiod
tags:
financegovernedai-ready
owner: finance-datav1.2 · 2024-Q4
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Single Source of Truth
AI-Ready Metrics
Governed by Design
Semantic layer implementation
One governed layer between your warehouse and every tool
The semantic layer defines business logic in one central place: what “revenue” means, how “active customers” is calculated, which dimensions apply to which metrics. Every BI report and every AI agent queries the same definitions.
New to the concept? Read our explainer what is a semantic layer. This page is about implementing one: how we design, build and run it on your stack.
How it fits in your stack
BI ToolsOmni, Looker, Tableau, Metabase
AI Agents & LLMsChatGPT, Claude, custom agents
Data Apps & APIsNotebooks, dashboards, exports
Semantic Layer
Metrics · Dimensions · Governance · Business Logic
Data Warehouse (Snowflake, BigQuery, Databricks…)
Start here
Choosing and standardising your semantic layer
When Everyone Has a Different Version of the Truth
A CFO stares at two different “Quarterly Revenue” numbers. Every time a new analyst joins or a new AI agent is plugged in, a new version of the truth is born somewhere in the stack.
“Your teams are arguing about numbers instead of acting on them.”
You'll recognize this
- Two different revenue figures in every board meeting. Finance says one thing, sales says another.
- AI tools are expensive and give inconsistent, untrusted answers across departments.
- New analysts reinvent the same metrics from scratch every quarter in a new spreadsheet.
- Data governance lives in PDFs nobody reads. Definitions drift, compliance risk grows silently.
Our Approach: Fast, Focused, and Built to Last
We don't do 6-month engagements with uncertain outcomes. We move fast, work with your existing stack, and deliver a working semantic layer. Not a slide deck.
01
Discovery & Diagnosis
A focused ~1 week research sprint. We map your current data stack, identify where metric definitions break down, and document the exact problem statements your team is facing. No fluff. Just a clear picture.
02
Semantic Layer Implementation
We implement the semantic layer directly into your environment, defining business logic in code, connecting it to your BI tools, data warehouse, and AI agents. Fixed-scope, no surprises.
03
Handover & Enablement
Every engagement ends with your team fully equipped to own what we've built. Documentation, training, and ongoing support options, so the layer grows with your business.
“After the first week, we understood that we had 39 different definitions of revenue across all departments. No wonder we got so many different outcomes from our AI.”
Head of Data Governance
Enterprise Client
The Impact: Before & After the Semantic Layer
Why it matters
Semantic interoperability in practice
One definition, everywhere
Revenue means the same thing in your BI dashboard, your AI chat interface, and your exported spreadsheet. No more metric drift between teams.
AI that queries meaning, not syntax
LLMs grounded in a semantic layer ask about business concepts, not raw SQL columns. The result is dramatically fewer hallucinations and wildly more accurate answers.
Governance without gatekeeping
Certified metrics live in one place. Any tool (BI, AI, notebooks, APIs) queries the same governed definitions. New tools get added without re-defining everything.
Interoperability by design
Your semantic layer becomes the translation layer between systems. Data warehouse, BI tool, AI agent, and data product all speak the same language.
The tooling landscape
Which semantic layer is right for you?
The market has several mature options, each with different tradeoffs on flexibility, governance, and BI integration. We're tool-agnostic and help you choose the right fit for your stack and team. Compare the two most common options in dbt Semantic Layer vs Cube.
dbt Semantic Layer
Best for dbt-native stacks
Cube
API-first, highly flexible
LookML / Looker
Google ecosystem
Omni
BI + semantic in one
Metriql
Open-source option
Atscale
Enterprise-grade
Lightdash
dbt-powered BI
Custom builds
When off-the-shelf doesn't fit
Not sure which one fits your situation? Book a free 30-minute call and we'll give you an honest, vendor-neutral recommendation.
Trusted Clientele Benefiting from our Consulting Experience
From the blog
Semantic layer resources
Semantic Layer9 min read
Apache Ossie: The Rename Is Not the Story
OSI is now Apache Ossie (Incubating). The rename is cosmetic. The governance transfer to the Apache Software Foundation is the story — and what it signals for the semantic layer as infrastructure.
Semantic Layer9 min read
Semantic Layer Consulting & Data Architecture in Amsterdam
Unwind Data is an Amsterdam-based data consultancy specialising in semantic layer implementation and data architecture for scale-ups and enterprises building AI. Independent, vendor-neutral, and practitioner-led.
Semantic Layer18 min read
AtScale vs dbt Semantic Layer: Enterprise vs Engineering-First
AtScale and dbt Semantic Layer both promise a single source of truth for metrics. But they represent two completely different architectural philosophies and serve two different organizational realities. Here is the head-to-head comparison that vendor demos will not give you.
Semantic Layer8 min read
Strategy Mosaic vs dbt Semantic Layer, Snowflake and Looker
Strategy Mosaic, the dbt Semantic Layer, Snowflake Semantic Views and Looker all promise one definition of revenue. They put your business logic in very different places.
Semantic Layer7 min read
Omni Pricing: What Omni Costs Compared to Other BI Tools
Omni does not publish a price list. How Omni pricing works, how it compares to Looker, Sigma, Tableau, Power BI and Strategy, and where BI cost really comes from.
Semantic Layer14 min read
Snowflake Horizon Context: What It Does to the OSI
Snowflake Horizon Context launched at Summit 26 and raised a question for the OSI coalition. Updated November 2026 with Apache Ossie, Business Ontology and what the first five months answered.
Semantic Layer10 min read
Gartner Semantic Layer Warning: AI Agents Will Fail Without Context
Gartner formally warned that skipping semantic foundations will cause AI agents to hallucinate, waste budget, and create governance risk. Practitioners already knew this. Here's what the context layer is and what building it actually requires.
Semantic Layer20 min read
How to Connect Your Semantic Layer to AI Agents: Architecture Guide
AI agents connected directly to the warehouse break in production. Here is the vendor-neutral architecture guide for connecting your semantic layer to AI agents using MCP, OSI, and A2A.
Semantic Layer15 min read
Looker Alternatives: The Architecture Decision Nobody Talks About
Evaluating Looker alternatives? The real decision is not which tool has better dashboards — it is what happens to your semantic governance layer when you switch. A vendor-neutral framework covering Omni, Lightdash, Sigma, Power BI, Metabase, Cube, and when NOT to leave Looker.
Semantic Layer9 min read
Semantic Layer vs Text to SQL: The Architecture Decision
Text-to-SQL accuracy nearly doubled between 2023 and 2026. The semantic layer still wins on determinism. But the real question isn't which benchmark wins — it's an architecture decision about where your business logic lives.
Semantic Layer19 min read
Snowflake Semantic View Autopilot: The Complete Practitioner Guide
How to get Snowflake Semantic View Autopilot working in production, not just in a demo. The decisions that matter, the limitations nobody mentions, and where SVA fits in your data architecture.
Semantic Layer7 min read
Omni's $120M Series C Puts the Semantic Layer at the Center of AI Analytics
Omni raised $120M at a $1.5B valuation today — with the semantic layer as the explicit center of their pitch and their moat. Here is why the framing matters as much as the number.
Semantic Layer15 min read
Snowflake Semantic Views: The Practitioner's Guide to Setup, Autopilot, and Best Practices
Snowflake Semantic Views are now the native semantic layer inside Snowflake, powering Cortex Analyst, AI agents, and BI tools from a single governed definition. Here is how to implement them correctly — including Autopilot, dbt integration, and the best practices that matter in production.
Semantic Layer11 min read
Semantic Layer for Multiple BI Tools: The Architecture That Ends Metric Drift
When you run Tableau, Power BI, and Sigma simultaneously, every metric gets defined three times — and diverges. A semantic layer for multiple BI tools is the only architecture that fixes this without replacing any of them.
Semantic Layer9 min read
Sigma vs Looker: The Semantic Layer Is the Real Decision
Most Sigma vs Looker comparisons debate visualizations and pricing. The actual decision is about the semantic layer — whether you need one, and where it should live relative to your BI tool.
Semantic Layer7 min read
Semantic Layer Consultant: What We Do and When You Need One
What does a semantic layer consultant actually do? When does it make sense to hire one versus building internally? And what makes an independent consultant different from the vendors pitching you tools? Here is the honest answer.
Semantic Layer23 min read
Best Semantic Layer Tool 2026: A Vendor-Neutral Comparison
The only vendor-neutral comparison of the best semantic layer tools in 2026. No product to sell. Covers dbt MetricFlow, Cube, AtScale, Snowflake Semantic Views, Databricks Metric Views, LookML, and Omni — organized by architecture fit, not feature score.
Semantic Layer13 min read
The Complete LookML Guide for 2026: Core Concepts and the dbt Migration Decision
A practitioner's guide to LookML in 2026: what views, dimensions, measures, and explores actually do, how LookML compares to the dbt Semantic Layer, and how to make the migration decision for enterprise teams.
Semantic Layer7 min read
Google Officially Separated Looker from Data Studio
Google just reversed the Looker Studio rebrand, reinstating Data Studio for the free tool and keeping Looker exclusively for enterprise governed analytics. For those of us who spent years explaining LookML to confused clients, this is overdue.
Semantic Layer7 min read
Why Omni's MCP Server Changes How You Query Data in Claude and Cursor
A practitioner's guide to connecting Omni's MCP server with Claude and Cursor. How the semantic layer transforms AI analytics from demo-ready to production-grade.
Semantic Layer7 min read
The dbt Semantic Layer: How MetricFlow Turns Business Logic Into Infrastructure
The dbt semantic layer with MetricFlow lets data teams define business metrics as code. Those definitions flow to any BI tool, AI agent, or analytics platform through a single governed interface.
Semantic Layer10 min read
Semantic Interoperability: The Open Standard Connecting Your Entire Data Stack
Semantic interoperability enables different systems, tools, and AI agents to share business logic consistently. The Open Semantic Interchange (OSI) specification is the vendor-neutral standard making it happen across your entire data stack.
Semantic Layer6 min read
The Semantic Layer Just Became an Industry Standard
Snowflake, Microsoft, dbt Labs, Salesforce, and Databricks all converged on the semantic layer as critical AI infrastructure within the same month. The Open Semantic Interchange spec is now the standard.
Semantic Layer9 min read
Semantic Layer BI Tools: Which Platforms Actually Govern Your Metrics
A comparison of BI tools with native semantic layers, from Omni and Looker to Domo's new 2026 addition. Evaluate which platform actually governs your metrics for AI readiness.
Semantic Layer30 min read
What Is a Semantic Layer? The Complete Guide for 2026
The semantic layer is the governed translation layer between your raw data and every tool that consumes it. Complete guide: definition, architecture patterns, vendor landscape, and why your AI agents cannot operate without one.
Semantic Layer9 min read
Omni vs Looker: The Complete BI Comparison for 2026
A detailed comparison of Omni vs Looker based on direct implementation experience. Covers features, pricing, migration, and which platform fits your organization.
Ready to Become the Architect of Your Company's Shared Reality?
Stop chasing conflicting numbers. Stop letting your AI guess. Let's map your current setup and build the layer that makes everything click.