Models

In Prophecy, a model comprises a set of gems that processes data into one output. Each model corresponds to a single table in your database.

Models follow the dbt build system and can run on either SQL fabrics or Prophecy fabrics. To work with the dbt build, each visual model is a SQL file stored in your project repository in Git.

Model language

Prophecy’s visual interface supports SQL models only. If you’d like to define Python models, you can still do so within the code interface.

Create models

To add a new model to your project:

1. Click + Add Entity from the bottom of Project tab of the left sidebar.
2. Click Model.
3. In the Add Model dialog, add a Model Name.
4. Review the path where the model will be saved in the project repository. In most cases, the default model path is sufficient.
5. Click Create.

This opens a new model canvas that is prepopulated with a Target model. Note that the dbt framework restricts models to one target output.

tip

While you can develop models visually using gems, you can also write models directly in the code view, which is automatically synced with the visual view.

Models vs pipelines

Models and pipelines are two different SQL project components. The following table describes the key differences between models and pipelines.

Feature	Models	Pipelines
Execution	SQL Warehouse	SQL Warehouse + Prophecy Automate
Sources and Targets	Native Tables/Models	Native Tables/Models + External Data
Outputs	Limited to one output	No limitation
Orchestration	External Only (Databricks Jobs, Airflow, etc.)	External OR Prophecy Automate

Show underlying models

Many visual transformations in pipelines are compiled into models under the hood. If you are working on a pipeline, you can view and edit the code of underlying dbt models in a pipeline. However, you cannot visually edit these underlying models.

To view these models, select Show Models from the ... menu in the left sidebar of a project.