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The dbt integration is currently in preview. Reach out to your account manager or contact us to have it enabled for your Cube account.
If your team already models data in dbt, you can import those models into Cube as cubes instead of redefining them by hand. dbt Pull connects to your dbt project’s Git repository, parses the project, converts each dbt model into a cube, and commits the generated files to a development branch — where you review them before they reach production.

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How it works

When you run a pull, Cube:
  1. Reads your dbt project from its Git repository.
  2. Converts each dbt model into a cube definition (one .yml file per model).
  3. Commits the generated files to your current development branch.
No connection to your data warehouse is made during a pull. Cube only reads your dbt project code and writes cube files — it never queries or modifies your warehouse.

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Prerequisites

  • A supported data warehouse. dbt Pull supports Snowflake, Redshift, and Postgres. If your deployment uses any other database type, the pull dialog will tell you it’s unsupported.
  • A Git repository containing your dbt project, reachable over HTTPS. (SSH remotes are not supported.)
  • A personal access token (PAT) with read access to that repository.
  • The dbt feature enabled for your account. If you don’t see the Integrations → dbt menu, contact Cube to have it enabled.
  • For the generated cubes to return data, the dbt models must already be built into your warehouse (via your normal production dbt run) in the schema you configure below. dbt Pull generates cube definitions that point at schema.<model>; it does not create the underlying tables.

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Connect your dbt repository

The dbt connection is configured on your deployment’s default data source.
1

Open the data source settings

Go to Settings → Data Sources and edit the default data source. Expand the dbt project (optional) section.
2

Fill in the connection fields

FieldDescription
Repository URLThe HTTPS clone URL of the repo that contains your dbt project, e.g. https://github.com/your-org/your-repo.git.
Access token (PAT)A personal access token with read access to the repository. Leave it unchanged to reuse the existing token.
Project pathThe path to the dbt project inside the repository (the folder containing dbt_project.yml). Use . if the project is at the repository root.
dbt target schemaThe schema your production dbt run builds models into (the schema of your production target in profiles.yml). Generated cubes will reference the tables in this schema.
3

Test the connection

Click Test connection. Cube checks that the repository is reachable with your URL and token and reports the result:
  • “Repository is reachable with this token” — you’re good to save.
  • An error message identifying the problem (invalid/expired token, repository not found, host unreachable, not a Git URL, SSH remote, etc.).
4

Save

Click Save dbt settings.
The access token is stored encrypted and is never returned to the browser. Saving these settings does not restart your deployment.

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Pull the models

1

Enter development mode

Open the deployment’s data model page (the IDE) and enter development mode. The dbt integration is disabled outside dev mode — the pull lands on your dev branch, never directly on production.
2

Open the pull dialog

Open the Integrations menu → dbt → Pull.
3

Set the output options

In the Pull from dbt Models dialog, configure how the cubes are generated:
OptionDefaultDescription
Path/model/cubes/dbtDirectory in the repository where the generated cube files are written.
Name prefixdbt_Prefix added to each generated cube’s name.
Title prefix(dbt) Prefix added to each generated cube’s display title.
Model selector(empty)Optional dbt selector using dbt ls --select syntax to limit which models are pulled, e.g. tag:cube or marts.*. Leave empty to pull all models.
Auto-detect primary keysOnMarks id / *_id columns (and columns with a dbt primary_key constraint) as the cube’s primary key.
Only pull martsOffWhen enabled, only pulls models whose path starts with the Marts folder value (e.g. marts).
If the output path already contains files, the dialog shows a warning with the file count: a pull overwrites generated cube files and deletes files in that folder that no longer correspond to a dbt model. See Re-running a pull.
4

Start the pull

Click Start Pull. A progress toast tracks the pull and ends with “dbt pull completed (N cubes)”. The generated files appear in your file tree under the output path, on your development branch.
Review the generated cubes, then commit and merge the branch through your normal workflow.

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What gets generated

dbt Pull converts models and their columns. dbt metrics, semantic models, and tests are not imported. For each dbt model in your project:
  • One cube is created (one .yml file per model), named <name prefix><model name> with title <title prefix><model alias or name>.
  • sql_table is set to the model’s fully-qualified relation, database.schema.model (empty parts are dropped, so e.g. Postgres yields schema.model).
  • Each column becomes a dimension. The dimension type is inferred from the column’s data_type where available, otherwise from the column name:
    dbt data_typeCube dimension type
    varchar, text, string, charstring
    integer, int, bigint, smallint, decimal, numeric, float, double, realnumber
    date, datetime, timestamp, timestamptz, timetime
    boolean, boolboolean
  • Column descriptions from your dbt project are carried over to the dimensions.
  • A count measure is added to every cube.
  • total_<column> sum measures are added for numeric columns whose names suggest an additive metric (names containing amount, price, cost, total, or value).
Models named metricflow_time_spine and any non-model resources (sources, seeds, snapshots, etc.) are skipped. Each generated file begins with a header noting that it’s auto-generated and recommending you don’t edit it by hand — use Cube’s extends functionality to layer customizations on top instead, so they survive the next pull.

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Re-running a pull

Pulling again refreshes the cubes to match your current dbt project:
  • Files for models that still exist in dbt are overwritten with freshly generated definitions.
  • Files in the output path whose models are no longer in the dbt project are deleted.
  • Files in the output path that correspond to a model still present in dbt are preserved — so a scoped pull (using a model selector or “Only pull marts”) will not delete the cubes for models outside that scope.
Because generated files are overwritten, manual edits to them are lost on the next pull. Keep customizations in a separate cube that extends the generated one.

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Limitations

  • Supported warehouses: Snowflake, Redshift, and Postgres only.
  • Imports models and columns only — not dbt metrics, semantic models, tests, exposures, or joins/relationships.
  • HTTPS + PAT authentication only — SSH remotes and other auth methods are not supported.
  • Pulls from the repository’s default branch.
  • Manual, on-demand — pulls are triggered from the UI; there is no scheduled or automatic sync.
  • One dbt project per deployment.

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Troubleshooting

The dbt feature isn’t enabled for your account. Contact Cube to enable it.
You’re not in development mode. Enter dev mode on the data model page — dbt Pull only runs against a dev branch.
Your deployment uses a database other than Snowflake, Redshift, or Postgres. dbt Pull isn’t available for it.
The dbt connection settings are missing or incomplete on the default data source. An account administrator can add them under Settings → Data Sources (see Connect your dbt repository). If you’re not an administrator, ask someone who manages the deployment to set it up.
The message identifies the cause:
  • Authentication failed — the token is invalid, expired, or lacks read access to the repository. Generate a new read-scoped token.
  • Repository not found — check the URL; for private repos this can also mean the token can’t see the repo.
  • Unsupported protocol / SSH remotes aren’t supported — use the repository’s HTTPS clone URL, not an SSH (git@…) URL.
  • Host unreachable / could not resolve host — check the URL; the host must be reachable over the public internet.
The error message describes what failed. Common causes:
  • The dbt project doesn’t exist at the configured Project path (no dbt_project.yml there).
  • A failure in the dbt project itself — the same failure you’d see running dbt locally.
Transient failures are retried automatically; persistent errors fail with a message describing the problem.
Your Model selector and/or Only pull marts filters excluded every model. The pull fails (rather than generating nothing and deleting files) and names the active filters. Adjust the selector/marts folder and try again. Confirm the selector with dbt ls --select <your selector> locally.
dbt Pull generates cube definitions that point at schema.<model>, but it doesn’t build the tables. Make sure your production dbt run has materialized the models into the dbt target schema you configured, and that the schema matches.
The sql_table is derived from your dbt project and the dbt target schema setting. Verify that dbt target schema matches where your models actually land, and that any per-model schema/database overrides in dbt are what you expect.