Why SqlDBM?

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Strategic advisors

Kent Graziano

Kent Graziano

The Data Warrior, Strategic Advisor, Data Vault Master, Author, Speaker, and Tae Kwon Do Grandmaster

Gordon Wong

Gordon Wong

Leading organizations through analytics transformations, preference for social missions, healthcare, energy, education, and civic engagement

Semantic Modeling

When every team defines metrics differently, no one trusts the numbers. SqlDBM brings analysts, BI tools, and AI engines into alignment around one governed semantic layer.

Layered semantic model panels — time dimension, synonyms, SQL expression, dimension, fact, filter, custom instructions, metric

A semantic layer your whole org can trust.

SqlDBM sits between your physical schema and your consumers, translating raw database objects into governed business concepts. Analysts, BI tools, and AI agents all draw from the same definitions. Your architects set the rules. Everyone else gets consistent answers.

Dim_Supplier mapped to the Supplier table — matching SupplierId, CompanyName, and Phone fields

01

Fields, metrics & filters.

Define reusable fields, metrics, and filters once. Every model, query, and AI tool draws from the same definitions instead of re-deriving them.

02

AI that speaks your business, not your schema.

Attach natural language instructions and sample queries to your models. Your AI tools get the context they need to return answers you can actually stake decisions on.

03

Switch between layers

Architects work at the schema level. Analysts and AI consume at the semantic level. SqlDBM keeps both synchronized automatically with no manual reconciliation.

Physical data types mapped to semantic types — int to Fact, string to Dimension, datetime to Time dimension

04

Auto-detect joins

PK/FK relationships automatically become semantic joins. The logic your architects built is exactly the logic your analysts and AI tools use.

05

Tables, pre-filled

Drag your tables in. SqlDBM pre-fills semantic attributes based on your schema context. Your team curates instead of constructing from zero.

Dim_Supplier with semantic attributes pre-filled and checked

Schema changes always in sync

Define default semantic attributes at the project level. All your Semantic Models inherit them automatically.

Project DefaultsPROJECTApplied to every Semantic Model in the projectSynonymsclients, cust_tablefxSQL ExpressionSUM(orders.amount)MetricTotalRevenueFilterregion = ‘NA’Set once, in any diagram — before a model exists.
Project DefaultsSalesSemantic Model✓ InheritedfxFinanceSemantic Model✓ InheritedfxMarketingSemantic Model✓ InheritedfxDefaults flow into every model automatically — no manual setup.
Semantic Model · OrdersSynonymsinheritedTotalRevenueinheritedRefundRateCustom metric“Explain refunds by region”Custom AI instruction+ Add custom attributeExtend one model without affecting the rest of the project.
Semantic Model · EU_OrdersLogical nameCustomerClientOverrideSemantic typeDimensionFactOverrideFilterregion = ‘NA’region = ‘EU’OverrideTotalRevenueinheritedOverride indicators show exactly what changed, per model.
EU_OrdersSemantic ModelFilter (override)region = ‘EU’model-levelProject DefaultInherited by all modelsFilter (default)region = ‘NA’project-levelPush ↑Reset ↓Promote an override to the default, or reset it. Sync flows both ways.

Round-trip your models. Nothing gets lost in translation

Every schema change is reviewed, compared, and controlled before it touches production.

01

Import from existing

Start from where you are. Import your existing database or YAML. SqlDBM fills in the gaps automatically, so your team isn’t rebuilding from scratch.

02

Compare

See exactly what’s changing before it goes live. Every property, every model reviewed and approved before it reaches your stack.

03

Export to YAML

Export governed models to YAML for version control, CI, and downstream tools. What ships is exactly what was reviewed — no drift between design and deployment.

Semantic View SNOWFLAKE.SALES.CUSTOMER Governed DIMENSIONS AbcCustomerNameDimension AbcRegionDimension MEASURES & TIME #TotalRevenueFact OrderCountMetric OrderDateTime dimension Definitions authored once in SqlDBM, deployed as a native Snowflake Semantic View. AND MORE ACROSS YOUR STACK + more

Full support for Snowflake Semantic Views, and more

SqlDBM brings native support for Snowflake Semantic Views and the broader ecosystem your team already relies on. No workarounds, no retrofitting.