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https://github.com/nicholasyager/dbt-loom

A dbt-core plugin to weave together multi-project dbt-core deployments
https://github.com/nicholasyager/dbt-loom

dbt dbt-core plugin python3

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A dbt-core plugin to weave together multi-project dbt-core deployments

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# dbt-loom

[![pypi version shield](https://img.shields.io/pypi/v/dbt-loom)](https://img.shields.io/pypi/v/dbt-loom)

dbt-loom is a dbt Core plugin that weaves together multi-project deployments. dbt-loom works by fetching public model definitions from your dbt artifacts, and injecting those models into your dbt project.

```mermaid
flowchart LR

classDef black fill:#f2f2ebff, stroke:#000, color:#000
classDef background fill:#f2f2ebff, stroke:#000, color:#000
classDef hidden fill:#BADC3F, stroke:#BADC3F, color:#BADC3F

style TOP fill:#BADC3F, stroke:#000

subgraph TOP[Your Infrastructure]
direction TB
dbt_runtime[dbt Core]:::background
proprietary_plugin[Open Source Metadata Plugin]:::background

files[Local Files]:::background
object_storage[Object Storage]:::background
discovery_api[dbt Cloud APIs]:::background

discovery_api --> proprietary_plugin
files --> proprietary_plugin
object_storage --> proprietary_plugin
proprietary_plugin --> dbt_runtime
end

Project:::black --> TOP --> Warehouse:::black
```

dbt-loom currently supports obtaining model definitions from:

- Local manifest files
- dbt Cloud
- GCS
- S3-compatible object storage services
- Azure Storage

:warning: **dbt Core's plugin functionality is still in beta. Please note that this may break in the future as dbt Labs solidifies the dbt plugin API in future versions.**

## Getting Started

To being, install the `dbt-loom` python package.

```console
pip install dbt-loom
```

Next, create a `dbt-loom` configuration file. This configuration file provides the paths for your
upstream project's manifest files.

```yaml
manifests:
- name: project_name
type: file
config:
path: path/to/manifest.json
```

By default, `dbt-loom` will look for `dbt_loom.config.yml` in your working directory. You can also set the
`DBT_LOOM_CONFIG_PATH` environment variable.

### Using dbt Cloud as an artifact source

You can use dbt-loom to fetch model definitions from dbt Cloud by setting up a `dbt-cloud` manifest in your `dbt-loom` config, and setting the `DBT_CLOUD_API_TOKEN` environment variable in your execution environment.

```yaml
manifests:
- name: project_name
type: dbt_cloud
config:
account_id:

# Job ID pertains to the job that you'd like to fetch artifacts from.
job_id:

api_endpoint:
# dbt Cloud has multiple regions with different URLs. Update this to
# your appropriate dbt cloud endpoint.

step_id:
# If your job generates multiple artifacts, you can set the step from
# which to fetch artifacts. Defaults to the last step.
```

### Using an S3-compatible object store as an artifact source

You can use dbt-loom to fetch manifest files from S3-compatible object stores
by setting up ab `s3` manifest in your `dbt-loom` config. Please note that this
approach supports all standard boto3-compatible environment variables and authentication mechanisms. Please see the [boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#environment-variables) for more details.

```yaml
manifests:
- name: project_name
type: s3
config:
bucket_name:
# The name of the bucket where your manifest is stored.

object_name:
# The object name of your manifest file.
```

### Using GCS as an artifact source

You can use dbt-loom to fetch manifest files from Google Cloud Storage by setting up a `gcs` manifest in your `dbt-loom` config.

```yaml
manifests:
- name: project_name
type: gcs
config:
project_id:
# The alphanumeric ID of the GCP project that contains your target bucket.

bucket_name:
# The name of the bucket where your manifest is stored.

object_name:
# The object name of your manifest file.

credentials:
# The OAuth2 Credentials to use. If not passed, falls back to the default inferred from the environment.
```

### Using Azure Storage as an artifact source

You can use dbt-loom to fetch manifest files from Azure Storage
by setting up an `azure` manifest in your `dbt-loom` config. The `azure` type implements
the [DefaultAzureCredential](https://learn.microsoft.com/en-us/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python)
class, supporting all environment variables and authentication mechanisms.
Alternatively, set the `AZURE_STORAGE_CONNECTION_STRING` environment variable to
authenticate via a connection string.

```yaml
manifests:
- name: project_name
type: azure
config:
account_name: # The name of your Azure Storage account
container_name: # The name of your Azure Storage container
object_name: # The object name of your manifest file.
```

### Using environment variables

You can easily incorporate your own environment variables into the config file. This allows for dynamic configuration values that can change based on the environment. To specify an environment variable in the `dbt-loom` config file, use one of the following formats:

`${ENV_VAR}` or `$ENV_VAR`

#### Example:

```yaml
manifests:
- name: revenue
type: gcs
config:
project_id: ${GCP_PROJECT}
bucket_name: ${GCP_BUCKET}
object_name: ${MANIFEST_PATH}
```

## How does it work?

As of dbt-core 1.6.0-b8, there now exists a `dbtPlugin` class which defines functions that can
be called by dbt-core's `PluginManger`. During different parts of the dbt-core lifecycle (such as graph linking and
manifest writing), the `PluginManger` will be called and all plugins registered with the appropriate hook will be executed.

dbt-loom implements a `get_nodes` hook, and uses a configuration file to parse manifests, identify public models, and
inject those public models when called by `dbt-core`.

## Known Caveats

Cross-project dependencies are a relatively new development, and dbt-core plugins
are still in beta. As such there are a number of caveats to be aware of when using
this tool.

1. dbt plugins are only supported in dbt-core version 1.6.0-b8 and newer. This means you must be using a dbt adapter
compatible with this version.
2. `PluginNodeArgs` are not fully-realized dbt `ManifestNode`s, so documentation generated by `dbt docs generate` may
be sparse when viewing injected models.