Modeling Workbench
- Semantic authoring is in public beta and available for Team and Enterprise plans.
- You need the Admin or Manager workspace role to create and edit semantic projects.
Semantic authoring lets you create and manage semantic resources directly in Hex — no third-party tool required. Semantic projects define a set of data models containing measures, dimensions, and relationships to provide a consistent, governed source of truth for self-serve analytics and AI across your workspace.
Create a semantic project
- Navigate to Settings > Data Sources > Semantic projects.

- Click + Semantic project.
- Enter a name, description, and select a data connection.
Use the Modeling Workbench
The Modeling Workbench is where you define all the data models and views in your semantic project, as well as their relationships. You can create data models based on existing tables and their columns, use inline validation and autocomplete to help build your YAML file, and use the publish preview to review changes and understand their impact before deploying.
Create a model
- In the Modeling Workbench, click Create a model.
- Add a Name and Description.
- Choose a table or query as the data source.
- Use Choose table or the Data Browser on the left sidebar to explore your warehouse.
- The default data source is a table, but you can also use a SQL query if you need to do any final filtering or transformation.
By default, Hex imports all columns from the underlying table as dimensions and imports column descriptions.

Each model is defined as a YAML configuration file, with fields specifying:
- Source table
- Dimensions
- Measures
- Relationships
When editing the YAML, the Modeling Workbench provides autocomplete suggestions for fields to help you write models and views faster and with fewer errors. Inline validation flags issues — such as missing definitions or incorrect formatting — so you can fix them before publishing.
See YAML specification for a detailed developer reference guide.

Semantic Views
Semantic views provide curated entry points into your semantic project, making complex data models more accessible. While models define all of your measures, dimensions, and relationships, semantic views let you select and organize a subset of those fields into a focused, user-friendly interface.
Views are optional, but helpful when you want to:
- Expose only the fields relevant for a particular analysis or audience
- Provide clearer names or descriptions without altering the underlying model
- Control how users navigate relationships across entities
- Create focused, purpose-built interfaces for different business domains
Views are presentational only - they don’t affect underlying query logic, but they shape how users browse and work with the modeled data. For example, a “Sales Performance” view might expose only certain revenue metrics and customer dimensions from a broader eCommerce model. This keeps the experience streamlined for sales teams and ensures users interact with the modeled data according to the definitions established by the data team.
Once published, semantic views appear in the Data Explorer and anywhere semantic projects are used across your workspace. See View specification for more details.
