> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getcollate.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Setup Multiple dbt Projects
> Set up multiple dbt projects in ingestion workflows to orchestrate metadata extraction from distinct models and teams.
# Setup Multiple dbt Projects
Collate supports ingesting metadata from multiple dbt projects in a single workflow — each project can have its own `manifest.json`, `catalog.json`, and `run_results.json` files.
**Tip**: Multiple dbt project support is available for S3, GCS, and Azure configurations only.
Before you configure the workflow, organize your dbt files so Collate can find them:
* Place each project's files in its own directory and name them `manifest.json`, `catalog.json`, and `run_results.json`.
* If your dbt tests produce multiple `run_results.json` files, keep them in the same directory as their corresponding `manifest.json`. Add a unique suffix to each file name — for example: `run_results_one.json`, `run_results_two.json`, `run_results_three.json`.
Collate scans the prefix path you specify and traverses each subfolder to locate the dbt files automatically.
## How to Configure Multiple dbt Projects (S3 Example)
Follow these steps to set up the dbt workflow for multiple projects using an S3 bucket.
### Step 1: Organize the dbt Files in Your S3 Bucket
Arrange each dbt project in a separate directory inside your bucket.
For example, here's a bucket named `dbt_bucket` with three dbt projects:
```
bucket_home/
├── dbt_files/
├── dbt_project_one/
│ ├── manifest.json
│ ├── catalog.json
│ └── run_results.json
└── dbt_new_projects/
├── dbt_project_two/
│ ├── manifest.json
│ ├── catalog.json
│ ├── run_results_one.json
│ ├── run_results_two.json
│ └── run_results_three.json
└── dbt_project_three/
├── manifest.json
├── catalog.json
└── run_results.json
```
### Step 2: Configure the dbt Workflow in Collate
Set the bucket and prefix path so Collate knows where to look.
* In the **dbt Bucket Name** field, enter your bucket name — in this example, `dbt_bucket`.
* In the **dbt Object Prefix** field, enter the path to the parent folder containing your projects — in this example, `bucket_home/dbt_files/`.
To scan the entire bucket, leave the **dbt Object Prefix** field empty.
### Step 3: Run the dbt Workflow
Trigger the workflow and verify all three projects are ingested.
After the workflow completes, Collate ingests the dbt metadata from all three projects in a single run.