Semantic models

Tip

Use dbt Copilot, available for dbt Enterprise and Enterprise+ accounts, to generate semantic models in the Studio IDE only.

Semantic models are the foundation for data definition in MetricFlow, which powers the Semantic Layer:

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Here we describe the Semantic model components with examples:

Semantic models components

The complete spec for semantic models is below:

The following example displays a complete configuration and detailed descriptions of each field:

Description

Includes important details of the semantic model. This description will primarily be used by other configuration contributors. You can use the pipe operator (|) to include multiple lines in the description.

Primary entity

You can define a primary entity using the following configs:

Entity types

Here are the types of keys:

• Primary — Only one record per row in the table, and it includes every record in the data platform.
• Unique — Only one record per row in the table, but it may have a subset of records in the data platform. Null values may also be present.
• Foreign — Can have zero, one, or multiple instances of the same record. Null values may also be present.
• Natural — A column or combination of columns in a table that uniquely identifies a record based on real-world data. For example, the sales_person_id can serve as a natural key in a sales_person_department dimension table.

Sample config

This example shows a semantic model with three entities and their entity types: transaction (primary), order (foreign), and user (foreign).

To reference a desired column, use the actual column name from the model in the name parameter. You can also use name as an alias to rename the column, and the expr parameter to refer to the original column name or a SQL expression of the column.

entity:
  - name: transaction
    type: primary
  - name: order
    type: foreign
    expr: id_order
  - name: user
    type: foreign
    expr: substring(id_order FROM 2)

You can refer to entities (join keys) in a semantic model using the name parameter. Entity names must be unique within a semantic model, and identifier names can be non-unique across semantic models since MetricFlow uses them for joins.

Dimensions

Dimensions are different ways to organize or look at data. They are effectively the group by parameters for metrics. For example, you might group data by things like region, country, or job title.

Dependencies

Related docs

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