## Set up dbt Core

URL: https://docs.atlan.com/apps/connectors/etl-tools/dbt/how-tos/set-up-dbt-core

> Configure access and storage for dbt Core in Atlan, including credential setup for AWS, GCP, and Azure, and uploading dbt metadata files.

This guide explains how to set up dbt Core in Atlan, including configuring access, organizing your storage bucket, and uploading the necessary metadata files so Atlan can process and analyze your dbt project data.

## Setup and access management 

In this section, learn how to configure access for dbt Core so Atlan can connect to your storage location and read the required metadata. Choose between using your own cloud storage bucket or an Atlan-managed bucket.

### Use your own bucket

Use this option if you store dbt artifacts in your own cloud storage bucket. You create a dedicated read credential for Atlan, then configure the connector in Atlan with your bucket details.

For the IAM role setup (requesting the service identity ARN, creating the IAM policy, and configuring the trust policy), follow [Connect dbt Core via a storage bucket](https://docs.atlan.com/llms/connectors/dbt/connect-dbt-core-via-a-storage-bucket/llms.txt)—Phase 2. Return here with your **Role ARN** to complete the Atlan configuration.

#### Step 1: Configure connector in Atlan

1. Go to **Marketplace** → search for **dbt** → click to set up dbt.
2. For **Source**, select **Core**.
3. For **Manifest Source**, select **External Object Storage**.
4. For **Storage Provider**, select **AWS**. Set **Authentication** to **IAM Role**.
5. Enter:
 - **AWS Role ARN**: the Role ARN from Phase 2 of the connect guide
 - **Bucket Name**: your S3 bucket name
 - **Prefix**: the path within the bucket where dbt artifacts are stored
 - **Region**: your bucket's AWS region
6. Click **Test Authentication** to verify, then proceed to configure the crawler.

### GCP (GCS)

#### Step 1: Create service account

1. In the [Google Cloud Console](https://console.cloud.google.com), go to **IAM & Admin → Service accounts**.
2. Click **Create service account** and give it a name (for example, `atlan-dbt-core`).
3. Grant the service account both of the following roles on the GCS bucket containing your dbt artifacts:
 - **Storage Bucket Viewer** (`roles/storage.bucketViewer`)
 - **Storage Object Viewer** (`roles/storage.objectViewer`)
4. Go to the service account's **Keys** tab → **Add key → Create new key → JSON**.
5. Download the JSON key file.

For more detail on GCS IAM setup, see [Set up Google Cloud Storage](https://docs.atlan.com/llms/connectors/google-gcs/set-up-gcs/llms.txt).

#### Step 2: Configure connector in Atlan

1. Go to **Marketplace** → search for **dbt** → click to set up dbt.
2. For **Source**, select **Core**.
3. For **Manifest Source**, select **External Object Storage**.
4. For **Storage Provider**, select **GCP**. Set **Authentication** to **Service Account**.
5. Enter:
 - **Project ID**: your GCP project ID
 - **Service Account JSON**: paste the full contents of the JSON key file
 - **Bucket Name**: your GCS bucket name
 - **Prefix**: the path within the bucket where dbt artifacts are stored
6. Click **Test Authentication** to verify, then proceed to configure the crawler.

### Azure (ADLS)

#### Step 1: Register app and create client secret

1. In the [Azure Portal](https://portal.azure.com), go to **Azure Active Directory → App registrations → New registration**.
2. Give the app a name (for example, `atlan-dbt-core`) and register it.
3. Note the **Application (client) ID** and **Directory (tenant) ID** from the overview page.
4. Go to **Certificates & secrets → New client secret**. Set an expiry and copy the secret **Value** immediately (shown only once).

For more detail on Azure Service Principal setup, see [Object storage for apps](https://docs.atlan.com/llms/platform/packages/object-storage-configuration/llms.txt).

#### Step 2: Grant access to storage container

1. In the Azure Portal, go to your **Storage account → Containers → your container**.
2. Click **Access Control (IAM) → Add role assignment**.
3. Assign the **Storage Blob Data Reader** role to the app registration created in Step 1.

#### Step 3: Configure connector in Atlan

1. Go to **Marketplace** → search for **dbt** → click to set up dbt.
2. For **Source**, select **Core**.
3. For **Manifest Source**, select **External Object Storage**.
4. For **Storage Provider**, select **AZURE**. Set **Authentication** to **Service Principal**.
5. Enter:
 - **Tenant ID**: Directory (tenant) ID from Step 1
 - **Client ID**: Application (client) ID from Step 1
 - **Client Secret**: the secret value from Step 1
 - **Account Name**: your Azure Storage account name
 - **Container Name**: the container holding your dbt artifacts
 - **Prefix**: the path within the container where dbt artifacts are stored
6. Click **Test Authentication** to verify, then proceed to configure the crawler.

### Use Atlan bucket (recommended)

Use this option if you prefer not to manage cloud credentials. Atlan provisions a dedicated storage prefix in its own bucket and provides you with write credentials so your pipeline can upload dbt artifacts directly.

#### Step 1: Request storage prefix from Atlan support

[Raise a support request](https://docs.atlan.com/llms/platform/submit-request/submit-request/llms.txt) and ask for:

- A dedicated **Atlan-managed storage prefix** for your dbt project
- Write credentials for your pipeline (IAM user for AWS, service account email for GCP, or equivalent for Azure)

Atlan provisions the prefix and shares the path and upload credentials with you.

#### Step 2: Update pipeline to upload artifacts

Once you receive your prefix, configure your CI/CD pipeline or dbt job runner to upload artifacts after each run using the upload credentials provided. See [Structure the bucket](#structure-the-bucket) below for the required directory layout.

Upload using standard CLI tools:

- **AWS**: `aws s3 sync ./target/ s3://<atlan-bucket>/<your-prefix>/<project>/<job>/`
- **GCP**: `gsutil -m cp -r ./target/ gs://<atlan-bucket>/<your-prefix>/<project>/<job>/`
- **Azure**: `azcopy sync ./target/ "https://<account>.blob.core.windows.net/<container>/<your-prefix>/<project>/<job>/"`

#### Step 3: Configure connector in Atlan

1. Go to **Marketplace** → search for **dbt** → click to set up dbt.
2. For **Source**, select **Core**.
3. For **Manifest Source**, select **Atlan Object Storage**.
4. Enter the **Object Storage Prefix** provided by Atlan support.
5. Proceed to configure the crawler.

## Structure the bucket 

Once you have configured access, the next step is to organize your storage bucket so that Atlan can correctly identify and process uploaded files.

> **Info**
>
> Atlan uses the `metadata.invocation_id` and `metadata.project_id` attributes to uniquely identify and link the uploaded files. Atlan doesn't use the file paths to identify a project or job that the file belongs to. The following directory structure is provided as a guideline.

Atlan supports extracting dbt metadata from multiple or single dbt projects. The `main-prefix` has the following format `gcs|s3:///` or `abfss:///`, if you used Atlan's bucket, the Atlan support team provides it after setting up access policies on your bucket.

> **Note**
>
>
> The `` (or `` for Azure) is optional. If your dbt project directories live at the bucket or container root, leave the **Prefix** field empty when you [configure the crawler](https://docs.atlan.com/llms/connectors/dbt/crawl-dbt/llms.txt) and place your project folders directly under the bucket or container.
>

You need to use the following directory structure, even if you have a single dbt project:

```
main-prefix
- project1
 - job1
 - manifest.json
 - other files
 - job2
 - manifest.json
 - other files
 - job4
 - manifest.json
 - other files
- project3
 - job5
 - manifest.json
 - other files
```

> **Warning**
>
> Atlan downloads every file under the configured prefix on each workflow run. The include and exclude folder filters are applied after the download and match only the first folder level under the prefix, so they skip processing of excluded folders but don't reduce download time or disk use. If the prefix also contains artifacts from previous runs, workflow runs slow down as the files accumulate and can fail with an out-of-disk-space error.

## Upload project files

To load correct metadata, Atlan processes the manifest.json and run_results.json files for each job. There are many ways to load the metadata, below are suggested approaches from Atlan. You need to upload the files from the target directory of the dbt project into distinct folders. Upload the run artifacts generated from the following commands:

- **(Required) Compilation results:**

<dd>

```bash
dbt compile --full-refresh
```

 This command generates files that contain a full representation of your dbt project's resources, including models, tests, macros, node configurations, resource properties, and more.

 **Files to upload:** `manifest.json` and `run_results.json`

 Alternatively, you can upload the same files by running the `dbt run --full-refresh` command.

</dd>

- **(Optional) Test results:**

<dd>

```bash
dbt test
```

 This command executes all dbt tests in a dbt project and generates files that contain the test results.

 **Files to upload:** `manifest.json` and `run_results.json`

</dd>

- **(Optional) Catalog:**

<dd>

```bash
dbt docs generate
```

 This command generates metadata about the tables and views produced by the models in your dbt project, for example, column data types and table statistics.

 **Files to upload:** `manifest.json` and `catalog.json`

</dd>

## Best practices for managing artifacts

Keep only the latest artifacts for each project and job under the configured prefix. As noted in [Structure the bucket](#structure-the-bucket), Atlan downloads every file under the prefix on each workflow run. Artifacts from previous runs make each workflow run slower and grow storage use. A job that runs every 15 minutes can add several GB per day if your pipeline writes a new timestamped folder for each run.

Atlan processes every `manifest.json`, `run_results.json`, and `catalog.json` under the prefix, not only the latest ones. dbt Core artifacts have no job identifier, so Atlan groups them by the `project_id` in each manifest and combines the results for each asset across all runs:

- For each asset, the result from the run with the most recent manifest `generated_at` timestamp wins, including its status. For tests, a run that executed the test always takes precedence over a run that only compiled it.
- For models, if the winning run has no owner, comment, table statistics, columns, or compiled code, Atlan keeps those values from an earlier run.

Re-running the same commands therefore rarely changes what appears in Atlan, but every older copy is still downloaded and parsed on each workflow run.

**Recommended:** provide only the latest run data for each job. Replace the artifacts after each run by syncing the output of each dbt command over the same project and job folders, so the prefix always holds one current copy of each file.

If you want to provide more data, or keep run history, in the configured prefix, use one of the following approaches:

- Add a lifecycle (cleanup) policy on the bucket that deletes old run folders after 7 days. Keep a `latest` copy of the artifacts for each job and exclude that folder from the policy.
- Write run history to a separate prefix, and point Atlan at a prefix that holds only the latest files.

> **Note**
>
> Keep a current copy of the artifacts for every job step, including steps that run rarely or have stopped running, for example a weekly job. Once old run folders age out, Atlan has no artifacts left for those steps and their results disappear from Atlan.

_Last updated: 8 October 2026._

---

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