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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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README 

        
# 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 and Remote 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

- Remote manifest files via http(s)

- dbt Cloud

- GCS

- S3-compatible object storage services

- Azure Storage



## Getting Started



To begin, 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 # This should match the project's real name

    type: file

    config:

      # A path to your manifest. This can be either a local path, or a remote

      # path accessible via http(s).

      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` environment variable.



## 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`.



## Advanced Features



### Loading artifacts from remote sources



`dbt-loom` supports automatically fetching manifest artifacts from a variety

of remote sources.



#### 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}

```



### Gzipped files



`dbt-loom` natively supports decompressing gzipped manifest files. This is useful to reduce object storage size and to minimize loading times when reading manifests from object storage. Compressed file detection is triggered when the file path for the manifest is suffixed

with `.gz`.



```yaml

manifests:

  - name: revenue

    type: s3

    config:

      bucket_name: example_bucket_name

      object_name: manifest.json.gz

```



### Exclude nested packages



In some circumstances, like running `dbt-project-evaluator`, you may not want a

given package in an upstream project to be imported into a downstream project.

You can manually exclude downstream projects from injecting assets from packages

by adding the package name to the downstream project's `excluded_packages` list.



```yaml

manifests:

  - name: revenue

    type: file

    config:

      path: ../revenue/target/manifest.json

    excluded_packages:

      # Provide the string name of the package to exclude during injection.

      - dbt_project_evaluator

```



## 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.