groq/dbt_artifacts

groq/dbt_artifacts

Language: Shell

License: Apache-2.0

Stars: 0

Forks: 2

Open issues: 0

Created: 2025-06-04T01:06:06Z

Pushed: 2025-06-05T18:00:38Z

Default branch: main

Fork: yes

Parent repository: brooklyn-data/dbt_artifacts

Archived: no

README:

dbt Artifacts Package

This package builds a mart of tables and views describing the project it is installed in. In pre V1 versions of the package, the artifacts dbt produces were uploaded to the warehouse, hence the name of the package. That's no longer the case, but the name has stuck!

  

Contributing

Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) and [MAINTAINERS.md](docs/MAINTAINERS.md) for more information.

Supported Data Warehouses

The package currently supports

• Databricks :white_check_mark:
• Spark :white_check_mark:
• Snowflake :white_check_mark:
• Google BigQuery :white_check_mark:
• Postgres :white_check_mark:
• SQL Server :white_check_mark:
• Trino :white_check_mark:

Models included:

dim_dbt__current_models
dim_dbt__exposures
dim_dbt__models
dim_dbt__seeds
dim_dbt__snapshots
dim_dbt__sources
dim_dbt__tests
fct_dbt__invocations
fct_dbt__model_executions
fct_dbt__seed_executions
fct_dbt__snapshot_executions
fct_dbt__test_executions

See the generated dbt docs site for documentation on each model.

Quickstart

1. Add this package to your packages.yml:

packages:
- package: brooklyn-data/dbt_artifacts
version: 2.9.3

:construction_worker: Make sure to fix at least the minor version, to avoid issues when a new release is open. See the notes on upgrading below for more detail.

2. Run dbt deps to install the package

3. Add an on-run-end hook to your dbt_project.yml

on-run-end:
- "{{ dbt_artifacts.upload_results(results) }}"

We recommend adding a conditional here so that the upload only occurs in your production environment, such as:

on-run-end:
- "{% if target.name == 'prod' %}{{ dbt_artifacts.upload_results(results) }}{% endif %}"

4. Run the tables!

dbt run --select dbt_artifacts

Notes on upgrading

Due to the structure of the project, when additional fields are added, the package needs to be re-run to ensure the tables include the new field, or it will simply error on the hook. These changes will always be implemented within a new minor version, so make sure that the version you use in packages.yml reflects this.

To upgrade and re-build, update the version number within packages.yml and then run:

dbt deps
dbt run --select dbt_artifacts

Make sure this is updated in any database that you use your code base in.

Configuration

The following configuration can be used to specify where the raw (sources) data is uploaded, and where the dbt models are created:

models:
...
dbt_artifacts:
+database: your_destination_database # optional, default is your target database
+schema: your_destination_schema # optional, default is your target schema
staging:
+database: your_destination_database # optional, default is your target database
+schema: your_destination_schema # optional, default is your target schema
sources:
+database: your_sources_database # optional, default is your target database
+schema: your sources_database # optional, default is your target schema

Note that model materializations and on_schema_change configs are defined in this package's dbt_project.yml, so do not set them globally in your dbt_project.yml (see docs on configuring packages):

> Configurations made in your dbt_project.yml file will override any configurations in a package (either in the dbt_project.yml file of the package, or in config blocks).

Environment Variables

If the project is running in dbt Cloud, the following five columns () will be automatically populated in the fct_dbt__invocations model:

• dbt_cloud_project_id
• dbt_cloud_job_id
• dbt_cloud_run_id
• dbt_cloud_run_reason_category
• dbt_cloud_run_reason

To capture other environment variables in the fct_dbt__invocations model in the env_vars column, add them to the env_vars variable in your dbt_project.yml. Note that environment variables with secrets (DBT_ENV_SECRET_) can't be logged.

vars:
env_vars: [
'ENV_VAR_1',
'ENV_VAR_2',
'...'
]

dbt Variables

To capture dbt variables in the fct_dbt__invocations model in the dbt_vars column, add them to the dbt_vars variable in your dbt_project.yml.

vars:
dbt_vars: [
'var_1',
'var_2',
'...'
]

Creating custom marts tables

Multiple modelled dim and fct models have been provided for ease of use, but we recognise that some use cases may require custom ones. To this end, you can disable all but the raw sources tables using the following in your dbt_project.yml file:

# dbt_project.yml

models:
dbt_artifacts:
+enabled: false
sources:
+enabled: true

In these sources tables, you will find a JSON column all_results which contains a JSON blob of the results object used, which you can use in your own analysis:

• exposures
• models
• seeds
• snapshots
• sources
• tests

This column can cause queries to become too long - particularly in BigQuery. Therefore, if you want to disable this column, you can make use of the dbt_artifacts_exclude_all_results variable, and set this to true in your dbt_project.yml file.

# dbt_project.yml
vars:
dbt_artifacts_exclude_all_results: true

Upgrading from 1.x to >=2.0.0

If you were using the following variables:

vars:
dbt_artifacts_database: your_db
dbt_artifacts_schema: your_schema

You must now move these to the following model configs:

models:
...
dbt_artifacts:
sources:…