Semantic Layer vs Traditional Data Marts

The semantic layer and the traditional data mart are two answers to the same enduring question: how do you give business users consistent, understandable, analysis-ready data? The data mart was the classic answer - build a curated, subject-specific subset of the warehouse for each team. The semantic layer is the centralized answer - define metrics and business meaning once, in a central layer that the consuming tools share. Understanding the difference is really understanding how analytics architecture has tried to fix the biggest weakness of independent marts: inconsistent metrics.

This comparison matters because the choice shapes whether an organization has one version of the truth or many. Both approaches make data more usable; only one of them inherently keeps definitions consistent across the whole business. As companies adopt BI, augmented analytics, and AI that all need to agree on what "revenue" means, the limitations of per-team marts have made a central semantic layer far more attractive.

The Problem Both Solve

Raw warehouse tables are not something a business analyst should query directly. They are modelled for integration, full of cryptic column names and join logic, and they contain no agreed notion of what "active customer" or "net revenue" means. Left to themselves, ten analysts will write ten slightly different queries for the same metric and get ten slightly different answers. Both the data mart and the semantic layer exist to close this gap between raw data and trustworthy business answers - they just close it in fundamentally different places.

The Traditional Data Mart Approach

A data mart closes the gap physically. The approach grew out of dimensional modeling, which Ralph Kimball introduced to the industry in 1996 with The Data Warehouse Toolkit. For each function - sales, finance, marketing - a team builds a dedicated, dimensionally-modelled subset of the warehouse containing just that function's data, pre-shaped into the tables and metrics it needs. The analyst queries the mart, not the warehouse, and gets fast, relevant, simplified data.

This works well within a team but has a structural flaw across teams: each mart encodes its own definitions in its own physical tables. The sales mart computes "revenue" one way; the finance mart computes it another. Both are internally consistent, but they disagree - and because the logic is baked into separate physical builds, reconciling them is painful. As marts proliferate, the organization accumulates many overlapping, slightly-inconsistent versions of the truth. This metric fragmentation is the classic weakness of independent marts. Kimball's own remedy was the bus architecture, in which conformed dimensions are managed once and reused across business processes, but that only works where every team builds against the shared model.

The Semantic Layer Approach

A semantic layer closes the gap logically and centrally. The idea is older than the term's recent popularity suggests: Business Objects patented its "universe" of business terms that generate SQL in a filing from 1991 (US patent 5,555,403, granted 1996). Instead of baking definitions into many physical marts, it defines each metric, dimension, and business term once in a central layer that sits between the warehouse and every consuming tool. When a connected BI tool, notebook, or AI agent asks for "revenue," it gets the one canonical definition - computed consistently, no matter who is asking or which of those tools they use.

The shift is from physical duplication to logical reuse. There is one definition of each metric, governed in one place, and every consumer inherits it. This directly solves the fragmentation problem: there cannot be three versions of "revenue" because there is only one definition for all of them to use. It is also why the semantic layer has become central to AI analytics - an AI agent answering questions in natural language is only trustworthy if it draws on governed, consistent definitions rather than guessing.

Data platforms and BI tools now ship their own semantic layers. The dbt Semantic Layer is powered by MetricFlow, which dbt Labs made available under the Apache 2.0 license in October 2025. Snowflake stores metrics, dimensions and facts in semantic views, Databricks defines them in Unity Catalog metric views, Looker uses LookML, and Power BI reports run on semantic models. In September 2025 Snowflake, Salesforce, dbt Labs and other partners announced the Open Semantic Interchange (OSI) to standardize how semantic metadata is defined and shared between tools. Each of these layers defines metrics well for the platform or tool it serves. An organization running several of them still needs one place where the business meaning behind each definition is agreed, owned and kept in step.

Head to Head

The two approaches differ on the dimensions that matter most for trustworthy analytics:

• Where logic lives. Mart: physically, in per-team tables. Semantic layer: logically, in one central definition.
• Consistency. Mart: prone to conflicting metrics across teams. Semantic layer: one definition, consistent everywhere - its core strength.
• Duplication. Mart: data and logic duplicated per mart. Semantic layer: define once, reuse.
• Autonomy vs governance. Mart: high team autonomy, weak central governance. Semantic layer: strong central governance, shared standards.
• AI-readiness. Mart: AI must guess which mart's definition is right. Semantic layer: AI draws on one governed definition - much more dependable.

Importantly, the two are not strictly either/or: data marts can still exist as a serving layer beneath a semantic layer, and many real architectures blend them. But the governance center of gravity has moved toward the semantic layer, because consistency at scale is what independent marts rarely delivered.

How Dawiso Approaches It

A semantic layer is only as trustworthy as the definitions inside it - and those definitions are business glossary entries: the agreed, governed meaning of "revenue," "active customer," "churn." This is where Dawiso fits the semantic-layer-vs-marts story. In the Dawiso business glossary, a business indicator carries its description, its calculation, an owner and a steward (attributes and components), and a workflow moves each definition through review and approval. Glossary terms are recognized and linked automatically wherever they appear in object content, so a mart's documentation points back to the definition it is meant to implement. Interactive data lineage, as far as lineage has been captured, then shows which tables, views and reports feed a given mart or dashboard, so a steward can compare what the mart computes with the approved definition - turning "whose revenue is right?" into a traceable, answerable question. AI agents can look up those definitions, object details and lineage through the Dawiso MCP server. Whether your serving layer is marts, a platform semantic layer, or both, governed definitions are what make the numbers agree.

Conclusion

The move from traditional data marts to the semantic layer is the story of analytics learning to keep its definitions consistent. Independent marts gave teams fast, autonomous access but let each one quietly invent its own version of the truth; the semantic layer answers the same need while defining each metric once for every tool connected to it, so the business - and every AI agent reading from it - shares one meaning of "revenue." The two can coexist, but the pattern is clear: consistency comes from governing definitions centrally, not from duplicating logic per team. Put the definitions in a governed business glossary, connect them to the data with lineage, and conflicting numbers become far easier to find and fix.

Sources

• Kimball Group, Dimensional Modeling Techniques (dimensional modeling introduced in 1996 with The Data Warehouse Toolkit).
• Kimball Group, Enterprise Data Warehouse Bus Architecture.
• Google Patents, US5555403A, Relational database access system using semantically dynamic objects (filed November 27, 1991, granted September 10, 1996).
• dbt Developer Hub, dbt Semantic Layer.
• dbt Developer Hub, About MetricFlow.
• dbt Labs, Announcing open source MetricFlow: Governed metrics to power trustworthy AI and agents, October 14, 2025 (MetricFlow under Apache 2.0, metrics for AI).
• Snowflake, Snowflake, Salesforce, dbt Labs, and More, Revolutionize Data Readiness for AI with Open Semantic Interchange Initiative, press release, September 23, 2025 (shared semantics for AI and BI).
• Snowflake Documentation, Overview of semantic views.
• Databricks, Unity Catalog metric views.
• Google Cloud, Introduction to LookML.
• Microsoft Learn, Semantic models in the Power BI service.
• Dawiso Help, Business glossary.
• Dawiso Help, Business glossary attributes and components.
• Dawiso Help, Workflows overview.
• Dawiso Help, Data lineage.
• Dawiso Help, Dawiso Model Context Protocol (MCP).