## Connect dbt Core via storage bucket

URL: https://docs.atlan.com/apps/connectors/etl-tools/dbt/how-tos/connect-dbt-core-via-a-storage-bucket

> Connect dbt Core to Atlan - your pipeline uploads dbt's output files to a cloud bucket (S3, GCS, ADLS, or Atlan-managed) and Atlan reads them.

dbt Core has no API for Atlan to call - instead your pipeline uploads dbt's output files to a cloud bucket and Atlan reads them. Five phases; the access phase differs by cloud provider.

## Prerequisites

- A pipeline (CI/CD, Airflow, cron…) that runs dbt and can upload files afterward.
- Someone who can create IAM roles / service accounts in your cloud account - usually your cloud or platform team.
- Admin or connection admin privileges in Atlan.

## Five phases at glance

| Phase | What happens | Owner | Typical time |
|---|---|---|---|
| 1. Choose storage | Your own bucket (S3/GCS/ADLS) or an Atlan-managed one | Atlan User + Cloud team | One decision |
| 2. Grant access | Give Atlan read access to the bucket | Cloud team + Atlan support | Hours–days (approvals) |
| 3. Upload artifacts | Pipeline writes dbt files in the expected layout | dbt / data team | 1–2 hours |
| 4. Configure & preflight | Enter bucket details in Atlan; run preflight (S3) | Atlan User | 30 minutes |
| 5. Run & verify | Crawl, check assets, schedule | Atlan User | 30 minutes |

## Phase 1 - Choose your storage

**Owner:** Atlan User + Your cloud team

| | Your own bucket | Atlan-managed bucket |
|---|---|---|
| **Good when** | You already have a bucket and an infra team comfortable with cross-account access | You want zero IAM work, or approvals on your side are slow |
| **Supported clouds** | AWS S3, Google Cloud Storage, Azure Data Lake Storage | Provisioned by Atlan ([raise a support request](https://docs.atlan.com/llms/platform/submit-request/submit-request/llms.txt)) |
| **What Atlan needs** | Read access (Phase 2) | Nothing - you get a prefix + upload credentials from support |
| **Preflight checks** | S3 only | Available (S3-backed) |

Details for both options: [Set up dbt Core](https://docs.atlan.com/llms/connectors/dbt/set-up-dbt-core/llms.txt). If you pick the Atlan-managed bucket, raise the support request now, skip Phase 2, and resume at Phase 3 when the prefix and credentials arrive.

## Phase 2 - Grant Atlan read access

**Owner:** Your cloud team

### AWS S3 - assume-role handshake

1. *Atlan User* - Ask Atlan support for the **Atlan dbt service identity ARN** for your tenant (it's tenant-specific).
2. *Cloud team* - Create an IAM policy granting read on the bucket and prefix:

```json
{
 "Version": "2012-10-17",
 "Statement": [{
 "Sid": "AtlanDbtReadAccess",
 "Effect": "Allow",
 "Action": ["s3:GetObject", "s3:ListBucket"],
 "Resource": [
 "arn:aws:s3:::example-dbt-artifacts",
 "arn:aws:s3:::example-dbt-artifacts/dbt/*"
 ]
 }]
}
```

3. *Cloud team* - Create an IAM role with that policy attached, and a **trust policy** naming Atlan's identity:

```json
{
 "Version": "2012-10-17",
 "Statement": [{
 "Effect": "Allow",
 "Principal": { "AWS": "<atlan-dbt-service-identity-arn>" },
 "Action": "sts:AssumeRole"
 }]
}
```

> **Caution: Two AWS traps seen repeatedly**
>
>
> **External ID:** if your security standard requires an `sts:ExternalId` condition in trust policies, confirm current behavior with Atlan support first - the connector hasn't always sent an external ID, so a trust policy requiring one can make AssumeRole fail.
>
> **KMS encryption:** if the bucket uses a customer-managed KMS key, also grant the role `kms:Decrypt` and `kms:DescribeKey` on that key - otherwise reads fail with AccessDenied even though the S3 policy is perfect.
>

### Google Cloud Storage

1. Create a service account and grant it `roles/storage.objectViewer` **on the bucket itself** (project-level grants on the wrong project are a recurring miss).
2. Generate a JSON key for it. You'll paste the key contents into Atlan in Phase 4, with the Project ID, bucket name, and prefix.

### Azure Data Lake Storage

1. Register an app in Azure AD; note the Application (client) ID and Directory (tenant) ID, and create a client secret.
2. Assign the service principal the `Storage Blob Data Reader` role on the storage account/container.
3. If the storage account has a **firewall**, see [Allowlists and firewalls](https://docs.atlan.com/llms/connectors/dbt/allowlists-and-firewalls/llms.txt) - Azure has a same-region quirk that makes IP allowlisting ineffective.

## Phase 3 - Upload dbt artifacts

**Owner:** dbt / data team

After each dbt run, your pipeline uploads the artifacts in this layout (one folder per project, one per job):

```
<main-prefix>/
├── project1/
│ ├── job1/
│ │ ├── manifest.json # required - from dbt compile/run --full-refresh
│ │ └── run_results.json # recommended - test outcomes
│ └── job2/
│ └── manifest.json
└── project2/
 └── job3/
 ├── manifest.json
 └── catalog.json # optional - from dbt docs generate (column details)
```

- Atlan identifies files by the `metadata.invocation_id` and `metadata.project_id` *inside* them - folder names are for your sanity, not Atlan's.
- Upload **fresh files every run**. Stale files = stale catalog, with no error anywhere.
- Keep run history out of the prefix. Atlan downloads every file under the prefix on each run, so old run folders slow Atlan down and can fill the disk. See [Best practices for managing artifacts](https://docs.atlan.com/llms/connectors/dbt/set-up-dbt-core/llms.txt).
- Keep names exactly `manifest.json` / `run_results.json` / `catalog.json` - renamed or missing manifests are a real failure mode.

## Phase 4 - Configure in Atlan and preflight

**Owner:** Atlan User

1. Open **New workflow → dbt Assets**, choose **Core**, then your cloud provider.
2. **Bucket name** - the plain name only, for example, `example-dbt-artifacts`. **Never include `s3://`** - the prefix-included form is a known silent breaker.
3. **Region** - the bucket's region (for example, `us-east-1`).
4. **Prefix** - the parent folder for your project folders; leave empty if projects sit at the bucket root.
5. Credentials: AWS - the **role ARN your team created** in Phase 2; GCS - project ID + service-account JSON; ADLS - tenant ID, client ID, client secret, storage account, container.
6. **S3 only:** click **Preflight checks**. It runs the "Manifest file check on S3" - confirming Atlan can list the bucket and see a manifest at your prefix. On GCS/ADLS there is no preflight; your first crawl is the test.
7. Set **Connection Admins** (empty = nobody can manage it later).

## Phase 5 - Run and verify

**Owner:** Atlan User

### Success checklist

- Preflight passes (S3) or the first crawl completes (GCS/ADLS)
- dbt models appear in Atlan with columns and tests
- A second crawl after your next dbt run picks up changes (proves the upload pipeline works end-to-end)
- The crawl is scheduled to run *after* your dbt jobs finish, not during

Anything failing → [Troubleshooting dbt connectivity](https://docs.atlan.com/llms/connectors/dbt/troubleshooting-dbt-connectivity/llms.txt), which is organized by the exact error text you'll see.

## Next steps

[Crawl dbt](https://docs.atlan.com/llms/connectors/dbt/crawl-dbt/llms.txt): Configure and run the crawler to extract metadata from dbt

_Last updated: 28 September 2026._

---

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