Semantic Layers in 2025

Semantic layers are exploding in popularity in 2025—but so is confusion. When every vendor claims to offer “semantics,” “ontology,” or an “AI-ready knowledge layer,” it becomes your problem to figure out what is a semantic layer in practice, which flavor you’re buying, and how it fits into your data catalog and governance strategy.

As a data catalog owner, data leader, or analytics engineering lead, you sit at the intersection of three converging trends:

• AI/BI convergence (copilots, agents, LLMs generating SQL),
• Data mesh and domain ownership,
• Multi-BI and multi-platform sprawl (Tableau + Power BI + notebooks + AI tools).

All of these depend on one thing: a reliable semantic data layer that defines what your business cares about—entities, metrics, joins, and policies—once, and reuses those definitions everywhere.

This playbook is written for you if:

• You own or influence the enterprise data catalog, and need it to stay the control plane of truth.
• You lead analytics, data engineering, or BI teams and must choose a semantic layer architecture.
• You’re being pitched everything from “semantic databases” to “agentic semantic layers” and want a rigorous way to cut through hype.

We will:

• Clarify what a semantic layer is (and is not) in modern analytics.
• Separate analytics semantics (metrics, joins, policies) from ontology/knowledge semantics (RDF/OWL, inference, global IDs).
• Give you a washing detector to spot semantic- and ontology-washing.
• Lay out semantic layer architecture options (BI-native, platform-native, universal/headless).
• Show how tools like Cube, platform-native semantics in Snowflake/Databricks, and BI semantic models fit into a maturity model.
• Provide reference architectures and a 90-day implementation roadmap anchored in catalog and governance realities.
• Illustrate how an integrated universal semantic layer using platforms like Coalesce + Cube can automate and govern semantics across Snowflake, Databricks, Fabric, BigQuery, and multiple BI tools.

TL;DR: (executive view)

You need one place where business meaning lives (metrics, entities, joins, governance)—and a plan for how other tools consume it.

Pick your center of gravity:

• Microsoft stack: Power BI semantic models in Fabric (Direct Lake + XMLA governance).
• Google stack: Looker’s LookML (with Tableau/Power BI connectors if hybrid).
• Snowflake-first AI: Semantic Views + Cortex Analyst semantic models.
• Databricks-first AI/BI: Unity Catalog Metric Views + LakehouseIQ.
• Multi-BI / Multi-platform: A universal/headless semantic layer (Cube, AtScale, GoodData).

Your catalog remains the control plane for discovery, lineage, ownership, and policy. Favor options that publish and subscribe metadata (tags, lineage, RLS) into it.

What is a semantic layer?

Featured definition (snippet-ready):

A semantic layer in data analytics is a business-friendly abstraction between your warehouse or lake and your BI/AI tools. It maps raw tables and columns into named entities, metrics, relationships, and policies so people and machines can query data using consistent business terms instead of technical schemas.

Put differently, a semantic layer (often called a semantic data layer or data semantic layer) is where you codify:

• What a Customer, Order, Product, Account, or Policy is.
• How Revenue, Active Users, Churn Rate, or Gross Margin % are calculated.
• How those entities are joined and at what grain.
• Which users are allowed to see which rows and columns (RLS/RBAC/ABAC).
• How natural language synonyms map to your data (e.g., “GM%” = “Gross Margin %”).

It sits logically between:

• Upstream: Data platforms (Snowflake, Databricks, BigQuery, Microsoft Fabric) and transformation pipelines (Coalesce)
• Downstream: BI tools (Tableau, Power BI, Looker, ThoughtSpot, Sigma, Omni), notebooks, AI assistants (Snowflake Cortex Analyst, LakehouseIQ, BI copilots), and applications.

You can think of this semantic layer as a shared contract: a set of machine-readable definitions and policies that every consuming tool can rely on. Modern platforms like the Coalesce semantic layer embody this contract by tying business meaning directly to governed transformation logic and metadata.

Core components of a modern semantic layer

Most robust semantic layers in 2025 include four building blocks:

• Entities & relationships
  Logical business objects (Customer, Order, Subscription, Store) and how they connect (Customer → places → Order). These typically map to fact and dimension tables or views in your warehouse/lake.
• Metrics & time logic
  Named calculations with clear grains, filters, and time semantics, for example:
  • revenue = sum(order_amount) where order_status = 'completed'
  • gross_margin_pct = (revenue - cogs) / revenue
  • Time intelligence like YTD, MTD, rolling 7/28/365 days, cohort logic.
• Governance & policies
  Access rules (row-level, column-level, object-level), masking policies, and data quality expectations enforced consistently across tools.
• Synonyms & NL metadata
  Human-language descriptors and synonyms used by LLMs and search interfaces to translate natural language questions into semantic queries.

How it differs from just SQL views or dashboards

A semantic layer is not just a collection of helpful views or dashboard formulas:

• Views are often scattered and undocumented, with weak owner mapping and no business glossary linkage.
• Dashboard logic lives inside reports, duplicated across tools and teams.
• A semantic layer is centralized, explicit, versioned, and testable:
  • Defined in code (YAML, JSON, LookML, DAX/Tabular, Cube schema).
  • Governed via Git, CI tests, and peer review.
  • Queried by many heads (Tableau, Power BI, Sigma, notebooks, AI/agents).

A concrete example

An e-commerce company defines:

• Entities: Customer, Order, Product.
• Metrics: Revenue, Refunded Revenue, Net Revenue, Customer Lifetime Value.
• Policies: Sales analysts see all geographies; EMEA managers see only EMEA rows.

Those definitions live in a semantic data layer (for example, a Cube schema, or Snowflake Semantic Views). Tableau reports, Power BI dashboards, Sigma workbooks, and an AI assistant (like Snowflake Cortex Analyst) all query that same semantic model, so “Revenue” and “CLV” mean the same thing everywhere.

A semantic layer is therefore not a nice-to-have; it’s the backbone that makes multi-BI, AI, and data mesh architectures trustworthy. If your team is also responsible for data discovery and lineage, you will typically pair the semantic layer with robust metadata management solutions that keep definitions, ownership, and usage fully transparent.

Semantics vs. ontology: key distinctions

• Taxonomy — controlled vocabulary & hierarchy (Product → Apparel → Shoes).
• Ontology — formal specification of concepts + relationships + constraints (RDF/OWL/SHACL).
• Knowledge graph — instantiated data aligned to the ontology with global IDs.
• Semantic layer (BI/metrics) — query-time abstraction for measures, joins, metrics, policies.
• Business glossary — human-readable definitions that map to semantics/ontology.

Modern analytics semantic layers borrow language from formal semantics and ontology work, but they solve a different problem. Ontologies and knowledge graphs live closer to semantic databases or graph stores (RDF triple stores, property graphs) that support inference, reasoning, and global identifiers across domains. A BI or data semantic layer lives on top of your warehouse or lake, and focuses on analytics correctness and governance instead of world reasoning.

When you hear “semantic database,” vendors typically mean a system that:

• Stores data as RDF triples or graph structures.
• Uses OWL/SHACL to define classes, properties, and constraints.
• Supports reasoning engines that can infer new facts from existing ones.

By contrast, a BI semantic layer is about:

• Getting “Gross Margin %” and “Active Customers” right.
• Making joins and time logic reusable.
• Ensuring RLS/RBAC policies are consistently enforced.
• Providing a machine-readable contract for BI and AI tools.

Where ontology lives vs. the BI semantic layer

• Your ontology/knowledge graph usually lives in a graph database or semantic database, owned by enterprise architecture, knowledge management, or data science teams.
• Your BI/data semantic layer usually lives in:
  • BI-native models (Looker LookML, Power BI Tabular, Tableau Semantics).
  • Platform-native constructs (Snowflake Semantic Views, Databricks Metric Views).
  • Universal/headless engines (Cube, AtScale, GoodData).

The key is to keep the two conceptually distinct:

• Ontology = conceptual truths and constraints about your world.
• Semantic layer = analytic truths (metrics, joins, filters, policies) used at query time.

Guardrail: a BI semantic layer is not an ontology. It encodes analytic intent, not world reasoning rules.

Scope and responsibilities

Most semantic confusion comes from mixing two layers of meaning:

• Analytic semantics (metrics, joins, drill paths, RLS).
• Knowledge semantics (concept hierarchies, constraints, inference).

If you treat them as the same thing, you create brittle architectures and unclear ownership. Catalog owners and data leaders need a crisp division of labor between:

• The analytic semantic data layer: implemented in BI-native, platform-native, or universal/headless semantic layer tools on top of your warehouse or lakehouse.
• The ontology/graph layer: implemented in semantic databases or graph systems, often managed by different teams.

Here’s how responsibilities typically break down.

Narrative view of responsibilities

• Metrics and time logic
  This belongs squarely in the analytics semantic layer. You want formulas, filters, and time intelligence to be:
  • Close to the data warehouse/lakehouse.
  • Versioned and tested like code.
  • Available to BI, notebooks, and AI assistants.

  Implement this in tools like LookML, DAX/Tabular models, Cube schemas, Snowflake Semantic Views, or Databricks Metric Views.

• Entity relationships (joins)
  Relationships—fact-to-dimension joins, many-to-many bridges, slowly changing dimensions—also live in the analytic semantic layer. They ensure every consumer can query “Customer → Order → Product” correctly without rewriting join logic.
  • These relationships can be mirrored in ontology/graph systems, but the place that BI and AI tools query is your BI/platform/universal semantic data layer.
• Inference (new facts)
  Inference (for example, “Managers are Employees, so Alice is an Employee”) is the domain of ontologies and knowledge graphs using OWL, rules engines, or SPARQL. This is not typically a function of BI semantic layers, which focus instead on deterministic metric evaluation and filtering.
• Constraints and validation
  Constraints like “Every Order must have exactly one Customer” or “DateOfBirth must be in the past” can be expressed:
  • Partly in the analytic layer (data tests, model constraints).
  • More formally in ontologies (via SHACL or OWL restrictions) or quality tools.

  BI semantic layers tend to enforce lightweight, analytics-focused constraints (for example, not-null, referential integrity checks behind the scenes).

• Policies (RLS/RBAC/ABAC)
  Access policies should be centralized and reused by every consumption tool. Practically, that means:
  • Implementing policies at the platform/catalog level (Snowflake Horizon, Unity Catalog) or in a universal semantic layer that multiple BI tools consume.
  • Making sure your catalog documents and distributes those policies.
• Global identifiers
  Global IDs (URIs) that uniquely identify entities across systems are a hallmark of knowledge graphs and semantic databases. Most BI semantic layers do not define URIs; they rely on warehouse keys. If you need cross-system identity resolution, that’s an ontology/graph responsibility, with the BI semantic layer referencing those IDs where needed.

Scope & responsibilities table (BI semantic layer vs ontology/graph)

Key implications for catalog owners and data leaders:

• Your analytic semantic data layer is built from semantic layer tools on top of your warehouse/lakehouse (BI-native, platform-native, or universal/headless).
• Your ontology/graph lives in a semantic database or graph store, which may enrich or inform the analytic layer but doesn’t replace it.
• Your catalog sits above both as the control plane for discovery, lineage, ownership, and policy—linking business glossary terms to both semantic models and ontologies.

Semantic washing detector: 10 evaluation tests

This checklist is a BS filter: it helps you distinguish between a true semantic/ontology system and marketing spin. Right now, a lot of vendors are “semantic-washing” (relabeling standard features as semantics) or “ontology-washing” (claiming AI reasoning without real formalism). Each item is a litmus test:

Red Flag: Semantic Washing

If a vendor claims to offer a “semantic layer” but can’t show you actual model files—YAML, LookML, JSON, or RDF/OWL—they’re almost certainly relabeling existing features. Real semantic layers are explicit, versioned, and testable. Always ask to see the definitions, not just the slideware.

1. Formalism: does it support RDF/OWL/SHACL?

Why it matters: Formal semantics require a standard modeling language (RDF for triples, OWL for ontologies, SHACL for constraints). If a tool can’t export/import these, it’s not really operating at the ontology/knowledge graph level.

2. Inference: can it derive new facts?

Example: If “Alice is a Manager” and “Managers are Employees,” then inference lets the system conclude “Alice is an Employee” without explicitly storing it. If a vendor’s “semantic layer” can’t reason over rules, it’s just a metrics layer.

3. Global IDs: are entities addressable via URIs?

In knowledge graphs, entities (Customer #123) should have a stable, global identifier (like a URI). This makes them reusable and linkable across systems. Without this, integrations get brittle.

4. Constraints: does it support cardinalities and validation?

True ontologies enforce rules: for example, “Every Order must have exactly 1 Customer.” If a system can’t validate data against such constraints, it’s not offering semantic rigor—it’s just descriptive metadata.

5. Separation: are analytic vs knowledge semantics decoupled?

BI semantics = “Gross Margin % = (Gross Margin ÷ Revenue).” Ontology semantics = “A Customer places an Order.” If a tool blurs these, you risk confusion. The test is whether you can tell where business logic lives vs. where conceptual knowledge lives.

6. Lineage: is round-trip supported?

Can you trace a metric/term back to its raw warehouse sources, transformations, and owners—and push updates downstream? True semantics integrate with lineage so you don’t get drift.

7. Policy semantics: are policies enforced everywhere?

Example: “Analysts can see US data, but not EU data.” If RLS (row-level security) or RBAC is only enforced in one tool, that’s weak. Real semantics propagate policies across all heads (BI, AI, notebooks, APIs).

8. Multi-head delivery: can it serve multiple consumers?

Does the layer feed multiple consumers (Tableau, Power BI, notebooks, AI agents) from the same definition? If not, you’ll end up duplicating definitions everywhere.

9. Versioned governance in code

Can you treat semantic definitions like code (YAML, JSON, LookML, DAX scripts), version them in Git, peer-review, test, and roll back? If not, you can’t scale governance.

10. Evidence over marketing: show me the model files

If a vendor claims a semantic layer but can’t show you the actual model files (schema, ontology, or metric definitions), they’re hand-waving. Always ask to see the YAML/LookML/OWL/JSON—not a slide.

Bottom line: These 10 tests separate real semantics/ontology systems from marketing hype.

• If most checks fail → it’s just a BI feature with a fancy label.
• If many pass → you’re looking at a true semantic or ontology-driven layer.

Choosing your semantic layer architecture

Your semantic layer architecture should be an explicit decision, not an accidental byproduct of whichever BI or data platform you adopted first.

In 2025, you’re usually choosing between three patterns:

• BI-native semantic layers inside one dominant BI tool.
• Platform-native semantic layers inside Snowflake, Databricks, Fabric, or BigQuery.
• Universal/headless semantic layers (Cube, AtScale, GoodData, etc.) that act as a headless metrics and entities hub.

The right choice depends on:

• Your primary data platform (Snowflake, Databricks, Fabric, BigQuery, multi-cloud).
• Your BI landscape (single-BI vs. multi-BI).
• Your governance requirements (regulatory pressure, centralized policy enforcement).
• Your AI/agent ambitions (LLM-assisted analytics, agents calling APIs).
• Your engineering maturity (Git/CI workflows, analytics engineering practice).

Architecture comparison at a glance

When BI-native is enough

Choose a BI-native business intelligence semantic layer when:

• One BI tool accounts for 90%+ of analytics usage.
• You’re at L1–L2 in the maturity model.
• Governance is primarily about controlling access within that BI environment.
• You don’t yet have large-scale AI/agent use cases.

Examples:

• Microsoft-first enterprise: Power BI + Fabric semantic models (Tabular/DAX) are the center of gravity; use XMLA for governance and external consumption.
• Google stack: Looker LookML as the semantic language; Tableau or Power BI connect via Looker’s Open Source Integration (OSI).

You can always add a universal layer later if you introduce a second BI tool or AI assistants.

When platform-native should be your default

Choose a platform-native semantic layer when:

• You are “all in” on Snowflake or Databricks as your data platform.
• You want governance bound tightly to the platform catalog (Snowflake Horizon, Unity Catalog).
• You care deeply about centralized RLS/RBAC, lineage, and tags.
• You plan to lean heavily on platform AI capabilities (Cortex Analyst, LakehouseIQ).

Examples:

• Snowflake-first bank or insurer: Build Semantic Views and Cortex Analyst models governed by Horizon Catalog to ensure consistent definitions and strong compliance.
• Databricks-first ML/AI organization: Define Metric Views in Unity Catalog and use LakehouseIQ to expose them to users via natural language and notebooks.

You can still front these platform-native semantics with a universal headless layer if you need standardized APIs or multi-BI governance later.

When to invest in a universal/headless layer

Choose a universal semantic layer architecture when:

• You have more than one major BI tool (for example, Tableau + Power BI + Sigma).
• You need to expose metrics to applications, partner portals, or AI agents, not just dashboards.
• You are adopting data mesh and want domain-owned semantics that still interoperate.
• You want to minimize BI lock-in by decoupling semantics from visualization.

Examples:

• Multi-BI SaaS company on Snowflake: Use Cube as a headless semantic data layer; feed Tableau, Power BI, and notebooks from the same metrics.
• Global enterprise with mixed Snowflake + Databricks: Use Coalesce for transformations and metadata, then Cube as a universal semantic layer that exposes metrics via SQL/REST/GraphQL across all platforms.

Coexistence and migration patterns

Architecture is rarely a one-time choice. Common patterns:

• Start BI-native → add universal later
  Begin with Power BI semantic models or LookML; once you introduce a second BI tool or AI assistants, stand up a universal semantic layer that either:
  • Reads from your BI model where possible, or
  • Reimplements key metrics in a headless engine and gradually migrates usage.
• Platform-native core → universal API surface
  Use Snowflake Semantic Views or Databricks Metric Views as the canonical source of truth. Add a universal semantic layer (for example, Cube) that reads those definitions or tables and exposes them to external apps and AI via APIs.
• Hybrid domain strategy
  Some domains are modeled in a semantic layer, others in platform-native semantics, but your catalog keeps everything discoverable and mapped to owners, policies, and lineage.

The key is to decide the center of gravity:

• If you’re Microsoft-first → Fabric/Power BI.
• If you’re Google-first → Looker.
• If you’re Snowflake-first → Snowflake Semantic Views + Cortex Analyst.
• If you’re Databricks-first → Databricks Metric Views + LakehouseIQ.
• If you’re polyglot or multi-BI → universal/headless (Cube, AtScale, GoodData).

Your catalog then orchestrates across whichever semantic layer tools you choose.

The three architectural patterns

A semantic layer translates warehouse/lake objects into business language—entities (Customers, Orders), relationships, metrics (Gross Margin), time logic (YTD/YoY), policies (row-level security), and NL synonyms—so humans and AI can query correctly without mastering raw schemas. Think “business truth + policy + context” exposed to BI, notebooks, agents, and apps.

From an architectural perspective, there are three broad semantic layer architecture patterns in 2025:

1. BI-native (semantics live in your BI)
   Looker (LookML), Power BI (DAX/Tabular), Tableau Semantics, ThoughtSpot Models/TML, Sigma Data Models, Omni Topics/Models.
2. Platform-native (semantics live in your data platform)
   Snowflake Semantic Views + Cortex Analyst; Databricks Unity Catalog Metric Views + LakehouseIQ.
3. Universal / headless (tool-agnostic)
   Cube, AtScale, GoodData, Kyligence.

Typical org profiles and trade-offs

• BI-native semantic layers
  Best for organizations where a single BI tool dominates, and you want a business intelligence semantic layer tightly integrated into that tool’s UX, modeling, and governance. It’s the simplest semantic data layer to stand up but offers limited multi-head reuse and can create lock-in if you later diversify BI or AI tools.
• Platform-native semantic layers
  Best when Snowflake or Databricks is your strategic data platform and you want semantics co-located with data governance (Horizon Catalog or Unity Catalog). This pattern gives you strong centralized control and makes AI-native features (Cortex Analyst, LakehouseIQ) more accurate, but it assumes strong commitment to one platform.
• Universal/headless semantic layers
  Best when you have multiple BI tools, data mesh domains, or you need semantics exposed via APIs to applications and AI agents. This pattern decouples semantics from any specific BI or data platform and acts as a true semantic data layer. It does, however, add another layer to run and govern.

We’ll now dig into each pattern and the major semantic layer tools within them, with guidance for catalog owners on lineage, discovery, and governance.

BI-native semantic layers

BI-native semantics are embedded in your primary BI platform. They give you an integrated modeling experience, strong alignment with dashboards, and a relatively low-friction way to get from L0: siloed reports to L1: BI-native metrics.

These BI-native models often become the de facto semantic layer architecture in single-BI organizations.

Looker (LookML)

• What it is:
  LookML is a declarative modeling language for defining views, Explores, joins, and metrics across your warehouse. It has a mature development workflow (Git-based), reusable dimensions/measures, and robust access controls.
• Use cases and strengths:
  • Ideal for Google stack or Looker-centric shops where LookML becomes the core metric store.
  • Rich developer ergonomics: Git branches, code review, tests.
  • Official connectors let Tableau and Power BI consume governed Explores, letting Looker act as a limited universal semantic layer.
• Where it fits in the maturity model:
  Often your first move to L1: BI-native metrics and sometimes L2 if other BI tools use Looker OSI.
• Catalog implications:
  • Treat LookML as the source of truth for metrics in Google-centric environments.
  • Sync Explore/view definitions, metric names, and owners into your catalog to align with the business glossary and lineage.
  • Use the catalog to document which LookML objects are canonical versus experimental.

Power BI (Fabric) semantic models

• What it is:
  Power BI semantic models (Tabular) and DAX measures, tightly integrated with Microsoft Fabric and OneLake. Direct Lake lets models query data in place. XMLA endpoints enable external consumption and governance.
• Use cases and strengths:
  • Best for Microsoft-first enterprises standardized on Power BI.
  • Strong object model (tables, relationships, measures, roles) and governance primitives (RLS, sensitivity labels).
  • Semantic models can be reused by Excel and other tools via XMLA.
• Where it fits in the maturity model:
  Core to L1 and can support L2 when external tools connect via XMLA.
• Catalog implications:
  • Pull metadata (tables, measures, roles) into your catalog, map to glossary terms, and annotate with owners.
  • Use catalog lineage to tie semantic models back to Fabric pipelines and upstream warehouse objects.

Tableau (Semantics + Virtual Connections)

• What it is:
  Tableau Semantics introduces shared semantic objects (relationships, join logic, entities) separate from individual workbooks. Virtual Connections centralize authentication, RLS, and data policies.
• Use cases and strengths:
  • For Tableau-first shops needing more centralized semantics and RLS across many workbooks.
  • Reduces workbook-level duplication of data sources and security logic.
• Where it fits in the maturity model:
  Moves a Tableau shop from L0 to L1, and closer to L2 when combined with external semantic layers.
• Catalog implications:
  • Sync Virtual Connections and data policies into the catalog so RLS is documented and auditable.
  • Treat Tableau Semantics objects as semantic entities and metrics; align with glossary naming standards.

ThoughtSpot (Models + TML)

• What it is:
  ThoughtSpot Models (successor to Worksheets) plus TML (ThoughtSpot Modeling Language). Marketed as an “Agentic Semantic Layer” built for natural-language search and AI-powered analytics.
• Use cases and strengths:
  • Built for search-driven analytics where NL questions map to semantic models.
  • TML supports import/export and version control.
• Where it fits:
  Strong L1 solution in ThoughtSpot-centric orgs, especially when AI search is a priority.
• Catalog implications:
  • NL accuracy depends heavily on consistent synonyms and definitions; catalog terms and ThoughtSpot models must align.
  • Ensure your catalog documents model ownership, usage domains, and RLS.

Sigma (Data Models)

• What it is:
  Sigma offers a spreadsheet-like UI with reusable Data Models. It has direct integration with semantic layer, letting Sigma consume metrics instead of redefining them.
• Use cases and strengths:
  • Great fit for organizations looking for self-service analytics without losing semantic rigor.
  • Business users work with familiar spreadsheet paradigms while analytics engineers manage models upstream.
• Where it fits:
  Works well at L1 (Sigma-native models) and L2 when used as a consumer of semantic layer or other headless semantics.
• Catalog implications:
  • Sigma Data Models  is the semantic source of truth.

Omni (Topics/Models)

• What it is:
  Omni is a BI platform using Topics/Models to define semantic structures on top of modeled data.
• Use cases and strengths:
  • Provides NL features and flexible modeling while aligning with transformations.
• Where it fits:
  Typically L1 or L2 depending on how much of the semantic logic is shared with other tools.
• Catalog implications:
  • Ensure the catalog knows that Omni Topics/Models are derived from code, with lineage to underlying Nodes and warehouse tables.
  • Align term ownership (for example, metric owners in Omni vs. enterprise catalog).

Across all BI-native semantic layers, the pattern is the same: semantics live inside the BI platform and primarily benefit that tool. For catalog owners, the job is to mirror those semantics into the catalog and prevent siloed metric definitions from proliferating across multiple BI stacks.

Platform-native semantic layers

Platform-native semantics push your semantic data layer down into the data warehouse or lakehouse itself, rather than leaving it in BI tools. This is where Snowflake and Databricks are heavily investing.

Benefits:

• Centralized governance: policies, tags, and lineage in one platform catalog.
• Closer coupling to performance (optimizers, caching, compute).
• First-class support for platform AI/BI features (Cortex Analyst, LakehouseIQ, Databricks Dashboards, etc.).

Snowflake semantic layer (Semantic Views + Cortex Analyst)

• Semantic Views:
  SQL objects that define logical entities (like Customers, Orders, Subscriptions) with join paths, metrics, and relationships. They essentially function as a data warehouse semantic layer, codified in SQL and metadata.
• Cortex Analyst:
  Snowflake’s LLM-powered analytics interface that turns natural language into SQL. Cortex Analyst uses YAML-based semantic models to understand entities, metrics, and synonyms, then generates queries against Semantic Views and underlying tables.
• Horizon Catalog:
  Snowflake’s governance and discovery layer (lineage, tags, RBAC, masking policies). Semantic Views and Cortex models are governed here, which means metrics and entities inherit consistent policies and lineage.

Why this matters to catalog owners and data leaders:

• Governance alignment:
  Semantics and governance live in the same platform as your data. RLS/RBAC/ABAC policies in Horizon apply uniformly to Semantic Views, dashboards, notebooks, and AI/LLM experiences.
• Lineage integration:
  Horizon can show lineage from raw tables → transformations → Semantic Views → consuming objects (dashboards, AI queries).
• AI readiness:
  By giving Cortex Analyst a rich semantic spec (YAML) plus Semantic Views, you significantly improve LLM-generated SQL accuracy and reduce hallucinations.

Interplay with universal semantic layers:

• You can define core entities and transformations in Snowflake (potentially using Coalesce to manage transformations and metadata).
• A universal/headless engine like Cube can then connect to those Semantic Views, generating a universal semantic layer that exposes metrics via APIs to multiple BI/AI tools.
• Your enterprise catalog ingests metadata from Horizon and from the universal layer to present a unified view.

Databricks semantic layer (Metric Views + LakehouseIQ)

• Metric Views (Unity Catalog):
  YAML-defined, centrally-governed metrics and entities stored in Unity Catalog. A Metric View defines measures, dimensions, filters, and time logic that can be queried from SQL, notebooks, BI tools, and Databricks Dashboards.
• LakehouseIQ:
  Databricks’ org-aware NL engine that learns from notebooks, SQL, dashboards, and Metric Views to answer natural language questions about your lakehouse. Metric Views are a key signal to LakehouseIQ; they tell it what metrics exist and how to compute them.
• Unity Catalog:
  Databricks’s governance and discovery layer. Metric Views, tables, volumes, and ML models are governed here with RBAC, data masking, and lineage tracking.

Why this matters for data semantic layer architecture:

• Single source of truth for metrics:
  Metric Views become the canonical metric store for Databricks workloads. BI tools (via SQL endpoints) can query Metric Views instead of embedding metrics in reports.
• Policy consistency:
  Unity Catalog policies apply everywhere Metric Views are consumed: notebooks, BI dashboards, AI/ML workloads, LakehouseIQ.
• AI/BI convergence:
  LakehouseIQ uses Metric Views to answer natural language questions with correct metric definitions, improving user trust.

Interplay with universal/headless semantic layers:

• You can treat Databricks Metric Views as a semantic substrate and then use a universal semantic layer (for example, Cube) to present them to external tools via REST/GraphQL/MDX.
• A transformation platform like Coalesce can model data and metrics in Databricks, with Metric Views and CDE/ETL Jobs captured as metadata, feeding downstream headless semantics.

For catalog owners: how to integrate platform-native semantics

• Configure bi-directional metadata sync between:
  • Snowflake Horizon / Unity Catalog and your enterprise data catalog (Collibra, Alation, Atlan, etc.).
  • Pull in Semantic Views / Metric Views as first-class objects with owners, SLAs, sensitivity tags.
• Use the catalog to:
  • Document which metrics are certified and which are domain-specific variants.
  • Show lineage from raw sources → Coalesce transformations → platform-native semantic objects → BI, AI, and notebooks.
  • Centralize policy documentation so business and audit teams can see where RLS/RBAC is enforced.

Platform-native semantic layers are particularly attractive when your governance strategy is platform-centric and you want your semantic data layer to live as close as possible to the data warehouse semantic layer itself.

Universal and headless semantic layers

Universal or headless semantic layers implement a semantic data layer that is intentionally decoupled from any one BI or data platform. They serve semantics to many “heads”:

• BI tools (Tableau, Power BI, Sigma, Omni, Looker, ThoughtSpot).
• Notebooks and data apps.
• AI assistants and agents (LLM-based tools, Snowflake Cortex Analyst, LakehouseIQ, custom agents).
• External or embedded applications via APIs.

A universal semantic layer is therefore a headless metrics and entities engine that sits on top of your warehouse/lakehouse and exposes:

• A unified semantic model (entities, metrics, joins).
• APIs for querying (SQL, REST, GraphQL, MDX).
• Caching and pre-aggregation for performance and cost control.

This pattern is especially valuable when you operate in a multi-BI, multi-platform environment, or you are pursuing data mesh and want domain-aligned semantic models that still share global metrics.

Cube universal semantic layer

• What it is:
  Cube is a universal semantic layer that sits above your warehouse/lakehouse and exposes metrics and cubes via SQL, REST, GraphQL, and MDX. It provides:
  • A semantic modeling language (Cube schema).
  • Pre-aggregations and caching for performance and cost optimization.
  • Role-based access control and integration with multiple BI tools and apps.
• APIs and multi-platform support:
  • Connects to Snowflake, Databricks, BigQuery, Microsoft Fabric, and others.
  • Exposes semantic data to:
    • BI tools via SQL/MDX (for example, Excel, Power BI, Tableau).
    • Custom applications via REST/GraphQL.
    • AI-driven experiences and agents.
• Why it matters:
  • Lets you define metrics once and serve them to any consumer, decoupling semantic logic from BI tools and data platforms.
  • Pre-aggregations significantly reduce compute costs and improve query response times—critical at enterprise scale.
  • Acts as a universal semantic layer for both human-driven BI and machine-driven AI use cases.

Other headless semantic layer tools (AtScale, GoodData, Kyligence)

• AtScale:
  • Enterprise semantic modeling language (SML).
  • Strong integration with enterprise BI tools and OLAP-like acceleration.
  • Focus on large enterprises with complex multi-BI environments.
• GoodData (Headless BI):
  • Offers a semantic model defined in MAQL (Modeling Analytical Query Language).
  • Exposes metrics via APIs and SDKs for embedded analytics and headless BI use cases.
  • Good fit when you need a semantic layer inside a SaaS product or portal.
• Kyligence:
  • OLAP-based semantic layer built on Apache Kylin heritage.
  • Strong for extremely large-scale analytical workloads in lake/lakehouse environments.
  • Integrates with multiple BI tools via connectors.

All of these tools share the same architectural idea: a headless semantic data layer that can serve many BI and AI consumers from a single definition.

How Coalesce + Cube deliver a universal semantic layer

Organizations with multiple BI tools and AI initiatives often struggle with one recurring problem: keeping metrics and definitions consistent across environments. Revenue looks different in Tableau vs. Power BI vs. a notebook; AI agents generate SQL against raw schemas, bypassing governance.

An integrated universal semantic layer using Coalesce + Cube addresses this by combining transformation, metadata, and headless semantics:

• Coalesce’s role (upstream semantic foundation):
  • Coalesce is a data transformation platform that models and orchestrates your transformations in Snowflake, Databricks, Fabric, or BigQuery.
  • It captures rich metadata—entities, joins, lineage, tests, and even metric logic—as code in Git-managed Projects.
  • Governance features (RBAC/ABAC security, Jobs, Nodes, Environments, Packages, Workspace) ensure transformations are well-documented, tested, and auditable.
• Cube’s role (universal semantic data layer):
  • Cube connects to the transformed objects in your warehouse/lakehouse.
  • It uses Coalesce’s metadata about tables, joins, and measures to automatically generate semantic models where possible.
  • It exposes that model via SQL, REST, GraphQL, and MDX APIs, plus caching and pre-aggregations for performance.
• Cross-platform reach:
  • The combination supports Snowflake, Databricks, Microsoft Fabric, and BigQuery, aligning well with multi-cloud and multi-platform strategies.
  • The same semantic definitions can be consumed by Tableau, Power BI, Sigma, Omni, notebooks, custom apps, and AI assistants (including Snowflake Cortex Analyst and other LLM-based tools).
• Key business benefits:
  • Automated semantic layer generation from metadata: Reduces manual modeling effort and keeps semantics synchronized with transformations.
  • Unified metrics across the organization: Eliminates metric discrepancies by centralizing definitions, regardless of BI tool.
  • Performance and cost optimization: Pre-aggregations and caching in Cube lower compute costs while improving response times.
  • AI/BI integration with governance: AI agents and BI users query the same governed semantic layer, respecting Coalesce-defined lineage and policies.

Conceptually:

Sources → Coalesce transformations → Warehouse/Lakehouse → Cube universal semantic layer → BI tools + AI agents + applications

For a concrete walkthrough of how this works in practice, the “Demos with Doug” session Peeling Back Semantic Layers with Coalesce and Cube shows the full lifecycle from transformation to headless semantics to BI and AI consumption. You can watch the Coalesce and Cube integration demo to see this universal pattern in action.

This pattern lets your catalog remain the control plane for ownership and policies, while Coalesce + Cube together implement a robust universal semantic layer architecture.

Universal/headless in multi-BI organizations: examples

• A global retailer on Snowflake uses:
  • Coalesce for domain-modeled transformations and governance.
  • Cube as the headless semantic layer.
  • Tableau for merchandising dashboards, Power BI for finance, and notebooks for data science.

  All of these heads query the same “Net Sales,” “Same-Store Sales,” and “Inventory Turns” metrics.

• A SaaS company with mixed Databricks and BigQuery workloads defines transformations and entities in Coalesce, then exposes a single universal semantic layer through Cube to:
  • Internal BI tools.
  • Customer-facing analytics embedded into their product.
  • AI copilots used by support and sales.

In both cases, the semantic data layer is universal and headless, even though the underlying platforms and BI heads vary.

Semantic layer tools comparison

To make the landscape of semantic layer tools more scannable, here’s a consolidated view across BI-native, platform-native, and universal/headless options.

Use this matrix to:

• Narrow your shortlist based on category (BI-native vs platform vs universal).
• Assess alignment with your primary platform and BI tools.
• Confirm the level of governance and AI/NL integration you actually need.

Semantic layer maturity model

This Maturity Model (L0–L5) provides data leaders a roadmap for how far along they are in building a semantic foundation — and what the next level of sophistication looks like. It’s not about vendor names per se, but about organizational capability. This maturity model is the “zoomed-out strategy lens”. It tells data leaders:

• Where you are today (be honest: are you L1, L2, or L3?).
• What investments move you up the ladder (for example, headless semantics → platform-native semantics → ontology/graph integration).

Higher levels require real semantics and ontology rigor, not just BI marketing terms. Here’s what each level means in practice:

L0: siloed reports

• Every BI team builds its own metrics directly in dashboards.
• “Revenue” is defined five different ways depending on the department.
• No semantic layer — just raw SQL or Excel logic.
• Risk: Inconsistent numbers, long debates in exec meetings.

L1: BI-native metrics

• You centralize definitions within one BI tool: for example, LookML in Looker, DAX in Power BI, Tableau Semantics.
• Metrics are consistent only inside that tool.
• Limitation: If you have multiple BI tools, each has its own version of truth.

L2: shared semantic layer across tools

• Introduce a universal or headless semantic layer (Cube, AtScale, GoodData) or use BI connectors (Looker’s OSI, Power BI XMLA).
• Now multiple heads (Tableau, Power BI, Sigma, notebooks, AI agents) can query the same definitions.
• Benefit: You start breaking free from tool lock-in.

L3: platform-native semantics with governance

• Definitions move closer to the data platform itself:
  • Snowflake → Semantic Views + Cortex Analyst.
  • Databricks → Unity Catalog Metric Views + LakehouseIQ.
• Governance (RLS, lineage, tags) enforced centrally by the platform’s catalog.
• Result: Stronger consistency and compliance; semantics are no longer “just a BI thing.”

L4: enterprise ontology/graph mapped to warehouse

• Formal ontologies (RDF/OWL/SHACL) and knowledge graphs are layered on top of the warehouse.
• Business concepts (Customer, Order, Product) have global identifiers and constraints.
• This connects analytic semantics (metrics) to enterprise-wide conceptual knowledge.
• Benefit: You can integrate across silos (ERP, CRM, warehouse) and enrich data with reasoning.

L5: reasoning-aware agents honoring both ontology + metrics

• AI agents or copilots can query data with awareness of both:
  • Analytic semantics (Gross Margin % formula, RLS policies).
  • Knowledge semantics (Customer ⟶ places ⟶ Order; PreferredCustomer ⊂ Customer).
• Agents can reason, validate, and enforce rules when generating answers.
• This is the “holy grail”: AI that respects definitions, governance, and context simultaneously.

Semantic layer maturity levels (summary table)

Use this table to honestly assess where you are today and what semantic layer architecture you should prioritize next.

Reference architectures for semantic layers

Semantic layer architecture choices shape your ability to enforce governance, support AI, and avoid vendor lock-in. While every organization has its quirks, most architectures fall into three patterns:

• Platform-centric (Snowflake or Databricks) — semantics embedded in your data platform.
• Universal/headless hub — a semantic data layer serving multiple BI tools, AI, and apps.
• BI-anchored — semantics living primarily in a single BI tool.

Quick takeaways:

• Platform-centric = best for orgs all-in on Snowflake or Databricks.
• Universal/headless = best for orgs with polyglot BI or needing API/AI access.
• BI-anchored = simplest for single-BI shops, but least future-proof.

Platform-centric (Snowflake or Databricks)

When to choose: You are “all-in” on one modern data platform, and governance/lineage at the platform level is a top priority.

• Definition lives in the platform:
  • In Snowflake, define Semantic Views (logical tables/entities) and Cortex Analyst YAML models.
  • In Databricks, define Unity Catalog Metric Views with joins, time logic, and KPIs.
• Distribution: BI and AI tools consume semantics directly via SQL, APIs, or connectors (Tableau, Power BI, ThoughtSpot, notebooks).
• Governance: Policies, lineage, tags, and access rules are enforced in Horizon Catalog (Snowflake) or Unity Catalog (Databricks).
• Benefits:
  • One semantic definition reused across every downstream tool.
  • Governance is centralized and enforced at the data platform, not downstream.
  • Strong fit for regulated industries and enterprise-wide “single source of truth.”
• Risks/Trade-offs:
  • Ties you tightly to one vendor’s stack.
  • BI features might lag behind universal layers in flexibility.

Metaphor: Your semantic layer is “part of the foundation” — embedded in the warehouse/lakehouse itself.

For catalog owners:

• Prefer this architecture when regulatory pressure demands platform-level controls and unified lineage.
• Ensure your enterprise catalog syncs platform metadata (Horizon/Unity) and marks platform-native semantics as authoritative for specific domains.

Universal / headless hub

When to choose: You operate in a multi-BI environment or want to expose metrics/APIs to apps and agents, not just dashboards.

• Definition lives in a universal layer:
  • Cube: APIs (SQL/REST/GraphQL/MDX) that feed BI, notebooks, apps.
  • AtScale/GoodData/Kyligence: enterprise semantic platforms with multi-head delivery.
• Distribution: One headless model feeds multiple BI heads (Tableau, Power BI, Sigma, Omni) + AI assistants + applications.
• Governance: Catalog documents metric ownership, SLAs, and lineage back to transformations; CI/CD workflows manage changes.
• Benefits:
  • Breaks BI lock-in. One semantic model serves many tools.
  • Flexible: also exposes metrics to APIs, embedded apps, AI agents.
  • Lets you swap BI vendors without losing semantic definitions.
• Risks/Trade-offs:
  • Extra infrastructure layer to maintain.
  • May duplicate some platform capabilities (lineage, governance).
  • Performance tuning and caching often required.

Metaphor: Your semantic layer is “the hub” — a central traffic controller that dispatches consistent metrics wherever needed.

How Coalesce + Cube support a universal semantic layer architecture

In a universal/headless hub pattern, you need two things to work together:

• A transformation and metadata platform to encode business logic and lineage.
• A universal semantic engine to expose that logic consistently.

Coalesce and Cube are designed to jointly implement this pattern:

• Coalesce:
  • Models transformations as Nodes within Projects in your Workspace, controlled by Jobs, Environments, and Packages.
  • Captures semantic metadata (entities, joins, business rules) in Git-native code.
  • Provides lineage, RBAC/ABAC security, and strong testing to ensure data reliability.
• Cube:
  • Reads from the transformed and curated datasets produced by Coalesce.
  • Leverages metadata to auto-generate or accelerate the creation of semantic models.
  • Exposes metrics and dimensions via SQL/REST/GraphQL/MDX APIs, complete with pre-aggregation and caching.

Together, they give you:

• A universal semantic data layer that operates across Snowflake, Databricks, Microsoft Fabric, and BigQuery.
• Consistent semantics for BI tools, AI tools (including Snowflake Cortex Analyst), and applications.
• Git-native governance and end-to-end lineage from raw sources → Coalesce Nodes → Cube semantic models → BI/AI consumption.

For catalog owners, this allows your catalog to remain the source of record for:

• Which semantic models are canonical.
• Which metrics are certified.
• Which policies and domains are mapped to which Coalesce Projects and Cube schemas.

BI-anchored

When to choose: Your org is dominated by one BI tool (90%+ usage) and the business is deeply invested in its workflows.

• Definition lives in the BI tool:
  • Looker: LookML views + Explores.
  • Power BI: Tabular/DAX models.
  • Tableau: Semantic Models + Virtual Connections.
• Distribution: Other tools consume via connectors or APIs (for example, Looker OSI → Tableau/Power BI; Power BI XMLA → Excel/Tableau).
• Governance: Catalog pulls in definitions for lineage & ownership, but BI is the source of truth.
• Benefits:
  • Simple and fast to implement.
  • Strong integration with the chosen BI’s native governance.
  • Fits companies that have standardized on a single BI.
• Risks/Trade-offs:
  • Metrics consistency doesn’t extend cleanly across other BI tools.
  • Harder to align with AI assistants, apps, or future BI diversification.
  • Risk of vendor lock-in.

Metaphor: Your semantic layer is “inside the app” — simple if you only need that app, fragile if you ever need more.

For catalog owners:

• This is a pragmatic short-term choice, especially for L1 organizations.
• Document BI-native metrics and semantic models in your catalog and flag them as BI-specific.
• If you foresee multi-BI or AI use, start planning a transition to platform-native or universal semantics.

Example archetypes mapped to architectures

• Snowflake-first bank in a regulated industry:
  • Architecture: Platform-centric with Snowflake Semantic Views + Cortex Analyst.
  • Catalog: Integrates Horizon metadata, tags regulatory-critical metrics and views, tracks lineage for audits.
• Multi-cloud SaaS company with Tableau + Power BI + notebooks:
  • Architecture: Universal/headless hub, using Coalesce + Cube for the semantic data layer.
  • Catalog: Centralizes metric definitions and ownership, regardless of BI tool.
• Microsoft-centric enterprise with heavy Power BI usage:
  • Architecture: BI-anchored (Fabric semantic models) with potential platform-native semantics as Fabric matures.
  • Catalog: Treats Power BI models as the core semantic layer, pulling them into the catalog for discovery and governance.

90-day implementation roadmap

Weeks 0–2: inventory glossary & top metrics (discovery + alignment)

Objective: Establish a minimum viable glossary and identify the metrics/entities that matter most.

• Business glossary:
  • Identify ~25 core entities (Customer, Order, Product, Region, etc.).
  • Capture business-friendly definitions, synonyms, and owners.
• Metrics inventory:
  • Prioritize ~50 key metrics (Revenue, Gross Margin %, Active Users, Churn Rate).
  • Document formula, grain, time logic (YTD, MoM, rolling averages).
• Governance foundation:
  • Assign owners/stewards for each metric/entity.
  • Map to warehouse tables and columns.

Output: A spreadsheet or catalog entry linking business terms → warehouse fields.

Goal: Everyone knows “the 50 metrics that count” and their business definitions.

Quick Win — Start With Your Top 20–50 Metrics

Do not boil the ocean on day one. Start by modeling the 20–50 metrics that drive most executive discussions—Revenue, Gross Margin %, Active Users, Churn. Define them once in your semantic layer, wire them into 2–3 BI tools, and use the visible consistency to build momentum for broader adoption.

For catalog owners (Weeks 0–2):

• Ensure each of the top entities and metrics has:
  • A glossary entry (definition, owner, synonyms).
  • A mapped set of source tables/columns.
• Tag candidate “certified” metrics that should become part of the semantic layer.
• Capture current BI reports that rely on those metrics for future migration.

Weeks 3–6: pilot semantic models in platform or universal layer (build + test)

Objective: Create a working semantic layer in your chosen architecture, and prove it works with multiple consumers.

• Platform-centric path (Snowflake/Databricks):
  • Define Semantic Views in Snowflake or Metric Views in Unity Catalog.
  • Add synonyms and time logic for Cortex Analyst or LakehouseIQ.
• Universal path (Cube, AtScale):
  • Encode metrics in the universal layer (YAML, JSON, or SML).
  • Stand up APIs/connectors for Tableau, Power BI, Sigma, Omni.
• BI-anchored path (Looker, Power BI):
  • Encode pilot domain in LookML or Tabular/DAX.
  • Use OSI/XMLA connectors for secondary consumption.

If you choose a universal/headless path, Coalesce and Cube give you a concrete pattern for the pilot. You can use Coalesce to codify your core domain—say, Sales & Revenue—into governed transformations with lineage, tests, and Git-based workflows in Snowflake or Databricks. Cube then layers on top, ingesting that metadata to build a semantic model with reusable metrics, dimensions, and pre-aggregations. From there, Tableau, Power BI, Sigma, or AI agents like Snowflake Cortex Analyst all query the same definitions. Because both layers are versioned as code, you can iterate quickly during the pilot while maintaining full auditability.

• Test consumers: Connect at least 3–5 downstream tools (for example, Tableau dashboard, Power BI report, Sigma worksheet, AI assistant query, notebook).

Output: One domain (for example, “Sales & Revenue”) available consistently across multiple heads.

Goal: Prove metrics can be defined once and consumed many times.

For catalog owners (Weeks 3–6):

• Register the new semantic models (platform-native, BI-native, or universal) in the catalog.
• Link them to the glossary terms and underlying warehouse/lakehouse tables.
• Begin tracking lineage from semantic models to consuming dashboards and AI tools.

Weeks 7–10: harden governance (RLS, lineage, catalog integration)

Objective: Move from proof-of-concept to enterprise-grade governance.

• Row-level security (RLS) & policies:
  • Centralize RLS at the platform (Snowflake Horizon, Unity Catalog) or BI semantic layer (Tableau Virtual Connections, Looker access filters).
  • Test policies across multiple heads to ensure consistency.
• Lineage & catalog integration:
  • Sync semantic layer definitions into your enterprise catalog (Collibra, Alation, Atlan, or Horizon/Unity directly).
  • Validate lineage: metric → semantic model → warehouse table → pipeline.
• Metadata tagging: Apply sensitivity, compliance, and ownership tags.

Output: Governance and security are consistent across BI, AI, and apps.

Goal: Metrics aren’t just consistent — they’re also compliant and auditable.

For catalog owners (Weeks 7–10):

• Confirm that:
  • RLS/RBAC/ABAC policies defined in the semantic layer are documented in the catalog.
  • Lineage from source systems → transformations (Coalesce) → semantic objects → dashboards/AI is visible end-to-end.
• Tag sensitive fields and metrics (PII, financial, regulated) and ensure that masking/security policies are linked.

Weeks 11–13: automate with CI/CD + AI assistants (scale + extend)

Objective: Treat semantics as code and extend usage to AI and automation.

• CI/CD for semantics:
  • Store definitions in Git (LookML, DAX scripts, YAML for Metric Views).
  • Add automated tests for formula accuracy, join logic, and access policies.
  • Enable peer review + pull requests.
• Docs & auto-publishing: Generate semantic documentation automatically (in catalog or Confluence).
• AI/NL integration: Connect semantics to assistants (Snowflake Cortex Analyst, Databricks LakehouseIQ, BI copilots) so natural language queries resolve consistently.
• Monitoring & alerting: Track semantic query usage, errors, drift.

Output: A production-ready semantic layer with CI/CD workflows and AI-friendly metadata.

Goal: Move from a pilot to a scalable, governed, and automated capability.

For catalog owners (Weeks 11–13):

• Ensure that semantic changes (new metrics, modified joins, updated policies) flow through Git/CI and are reflected in the catalog.
• Validate that AI assistants (Cortex Analyst, LakehouseIQ, BI copilots) are configured to use your semantic models, not raw schemas.
• Establish a change management process where catalog and semantic layer updates are synchronized (for example, PR templates requiring glossary updates).

Catalog Owner Checklist at Each Phase

As a catalog owner, verify at each phase: (1) Glossary entries and owners are updated for all modeled entities and metrics; (2) Lineage from source tables to semantic models is captured; (3) RLS/RBAC policies are documented centrally; (4) Semantic changes follow your Git/CI process.

By day 90, you should have:

• A trusted glossary + top 50 metrics.
• At least one pilot domain live across 3+ consumption tools.
• Centralized RLS/policy enforcement.
• Lineage flowing into your catalog.
• CI/CD automation and AI assistants consuming the same semantics.

Conclusion

Semantic layers are now foundational to modern data platforms—not just a nice abstraction for BI teams. In 2025, your ability to deliver trustworthy analytics and AI depends on building a robust semantic data layer and aligning it with your catalog and governance strategy.

This playbook has given you three key lenses:

• A washing detector to separate real semantics and ontology from marketing spin, keeping you honest about what each vendor actually delivers.
• A maturity model (L0–L5) to understand where your organization is today and what capabilities you need next (BI-native, universal/headless, platform-native, ontology, reasoning-aware agents).
• A set of semantic layer architecture patterns—BI-anchored, platform-centric, and universal/headless—so you can choose a deliberate path rather than drifting into semantic sprawl.

Across all of these, one principle stands out: analytics semantics and knowledge semantics are distinct but complementary. Your BI or universal semantic layer encodes metrics, joins, and policies; your ontology/graph encodes conceptual relationships and constraints. Your catalog ties them together as the control plane for discovery, lineage, and policy.

If you see yourself in the multi-platform or data mesh scenarios described here, it’s worth exploring an integrated universal semantic layer. The Coalesce + Cube partnership is designed for exactly this: Coalesce captures business logic, lineage, and governance in your transformations; Cube turns that into a universal semantic layer that serves BI tools, AI assistants, and applications. You still keep your enterprise catalog as the control plane for discovery and policy—but semantics become testable, versioned code rather than scattered dashboard logic. You can dive deeper into how this works in practice on the Coalesce semantic layer product page, which outlines the automation, governance, and multi-platform capabilities in more detail.

Whether you build it with these semantic layer tools or others, aim for that same pattern of automation, universality, and governance.

Semantic layers are critical, but they are not ontologies. By separating analytics semantics (metrics) from knowledge semantics (ontology/graphs), you avoid washing, maintain credibility, and lead with clarity. With a coherent semantic layer architecture and a strong catalog, you can support data mesh, multi-BI, and AI agents—without sacrificing trust in the numbers.