# Run data models from Console
> The guide to running data models in Snowplow.
> Source: https://docs.snowplow.io/docs/modeling-your-data/running-data-models-via-console/
> Documentation index: https://docs.snowplow.io/llms.txt

You can use [Snowplow Console](https://console.snowplowanalytics.com) to schedule and run [dbt](https://www.getdbt.com/) data models, including our [out-of-the-box models](/docs/modeling-your-data/modeling-your-data-with-dbt/dbt-models/), [automatically generated models](/docs/modeling-your-data/automatically-generated-data-models/), and any custom or third party models.

## Overview

```mermaid
flowchart LR
  subgraph console [Snowplow Console]
    schedule1(Schedule 1)
    schedule2(Schedule 2)
    schedule3(...)
  end
  subgraph "Your cloud account"
    job(Modeling job)
  end
  subgraph "Your Git repository"
    project(dbt project)
  end
  warehouse[(Warehouse)]
  console(Snowplow Console) -->|Trigger| job -->|Execute models| warehouse
  job -.->|Read configuration| project
```

To use this feature, you will need to store your data models in a **Git repository** organized into one or more [**dbt projects**](https://docs.getdbt.com/docs/build/projects). This provides you full control over the model logic and helps with versioning and collaboration.

In Console, you can define multiple **run configurations** and **schedules** to control what dbt commands get executed and when. For example, you could set the Unified Digital data model to run every day at midnight.

Based on what you defined, Console will trigger data modeling **jobs** in your cloud account associated with Snowplow (managed by you, in the case of Private Managed Cloud, or by Snowplow otherwise). The jobs will execute actual dbt commands against your data warehouse.

> **Tip:** This architecture allows you to take advantage of a secure connection to the warehouse (e.g., VPC Peering or AWS Private Link) that you might have already set up with Snowplow.

## Prerequisites

To get started, you will need a Git repository (and sufficient permissions to grant read access to Snowplow).

We also highly recommend installing dbt locally so that you can quickly test your model configuration. Follow the [dbt documentation](https://docs.getdbt.com/docs/core/installation-overview) to install dbt Core as well as the relevant adapter for your destination (e.g., `dbt-snowflake`).

## Initial setup

First, create a Git connection to your repository:

- In Console, go to **Settings > Connections > Set up connection > Git repository**
- The setup flow will guide you to create and test the connection

Next, create the model project:

- Navigate to **Modeling > Model projects > Create model project**
- You will need to provide the Git connection you created in the previous step, as well as some metadata for your project
- Console will show you the dbt commands to initialize your project if you haven't done so (you can also bring a pre-existing project)

Once the project is created, you can start adding data models.

## Add data models

> **Tip:** If you already have a dbt project with a set of data models, you can skip this section. Console will work with your current dbt configuration in `dbt_project.yml` and `packages.yml`.

Because the dbt configuration is stored in your Git repository, rather than in Console, you will add and configure models by making commits to this repository.

For example, to install our Unified Digital data model, you can follow the [tutorial](/tutorials/unified-digital/intro/) and add `snowplow/snowplow_unified` to your `packages.yml`.

Likewise, to configure variables for the models, edit the `dbt_project.yml` file.

With multiple models, you might run into a situation where one depends on the output of another. As long as you use [the `ref` function](https://docs.getdbt.com/faqs/Models/create-dependencies), dbt will automatically infer and honor dependencies. In particular, see how to set up our Unified Digital and Attribution models to [work together](/docs/modeling-your-data/modeling-your-data-with-dbt/dbt-models/dbt-attribution-data-model/keeping-cohesion-with-unified/).

> **Note:** When running the models, Console will override any warehouse connection settings with the ones you will configure in the next section.

Once you are happy with the dbt project setup, run dbt commands locally to verify it.

## Connect to the warehouse

In this step, you will create a warehouse connection for the models to use. Note that you can define more than one connection, for example, if you would like to have separate model run configurations for production and QA data.

> **Tip:** You might have already set up a warehouse connection for loading the data. Data modeling, however, requires a new connection, as you will often want data models to run under a different user/role and with different permissions.

Navigate to **Settings > Connections > Set up connection > Data modeling**. Select your warehouse and follow the steps in Console to create and test your connection.

## Add run configurations

For Console to run your data models, you will need to create run configurations.

Each run configuration controls what dbt commands will be executed and when. More concretely:

- You can add one or more run configurations to a project
- Each run configuration can contain one or more schedules
- For each schedule, you can define specific dbt commands

> **Tip: Examples**
>
> If you want to run all your models every day at midnight, you would create:
>
> - One run configuration
> - One schedule (daily, 12 AM)
> - Add standard dbt commands, e.g. `dbt deps` and `dbt run`
>
> If you want to run different models within the project at different times, you can use dbt’s [selector features](https://docs.getdbt.com/reference/commands/run#running-specific-models). In this case, create:
>
> - One run configuration
> - Two schedules, e.g. daily at 12 AM and every Monday at 3 AM
> - Pick different commands for each schedule, e.g. `dbt run --select <...>`
>
> In both cases, note that `dbt run` will automatically run all the relevant models in the correct order, [accounting for dependencies](https://docs.getdbt.com/faqs/Models/create-dependencies).

To create a run configuration:

- Navigate to your model project in Console
- Click **Add run configuration**
- Pick the warehouse connection created earlier
- Click **Add schedule**
- Select the desired time and dbt commands
- Repeat as necessary

## Run the models

Once you’ve defined the run configurations and schedules, Console will automatically run your data models. You can see when the next run is scheduled by looking at each run configuration.

![Run configurations panel showing an active \&quot;Main run configuration\&quot; on BigQuery with one schedule, and an \&quot;example-models\&quot; schedule set to run every 20 minutes with the next run today at 20:00](/assets/images/run-configurations-85b1a6e33da4d61b7835cbbda702f0aa.png)

You can also use the **Run now** button to trigger a one-off model run for a specific schedule. The resulting job will use the same commands and settings.

## Troubleshooting

See [Resolving data model failures](/docs/modeling-your-data/running-data-models-via-console/resolving-data-model-failures/).