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https://github.com/brooklyn-data/dbt_artifacts
A dbt package for modelling dbt metadata. https://brooklyn-data.github.io/dbt_artifacts
https://github.com/brooklyn-data/dbt_artifacts
dbt
Last synced: 3 months ago
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A dbt package for modelling dbt metadata. https://brooklyn-data.github.io/dbt_artifacts
- Host: GitHub
- URL: https://github.com/brooklyn-data/dbt_artifacts
- Owner: brooklyn-data
- License: apache-2.0
- Created: 2021-02-03T15:57:10.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-07-14T21:17:23.000Z (4 months ago)
- Last Synced: 2024-07-28T21:57:07.014Z (3 months ago)
- Topics: dbt
- Language: Shell
- Homepage:
- Size: 2.69 MB
- Stars: 313
- Watchers: 25
- Forks: 115
- Open Issues: 37
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-dbt - dbt-artifacts - This package builds a mart of tables from dbt artifacts loaded into a table. (Packages)
- awesome-starred - brooklyn-data/dbt_artifacts - A dbt package for modelling dbt metadata. https://brooklyn-data.github.io/dbt_artifacts (others)
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!
[![Main branch test package](https://github.com/brooklyn-data/dbt_artifacts/actions/workflows/main_test_package.yml/badge.svg)](https://github.com/brooklyn-data/dbt_artifacts/actions/workflows/main_test_package.yml)
[![Main branch lint package](https://github.com/brooklyn-data/dbt_artifacts/actions/workflows/main_lint_package.yml/badge.svg)](https://github.com/brooklyn-data/dbt_artifacts/actions/workflows/main_lint_package.yml)
[![Documentation](https://github.com/brooklyn-data/dbt_artifacts/actions/workflows/publish_docs_on_release.yml/badge.svg)](https://github.com/brooklyn-data/dbt_artifacts/actions/workflows/publish_docs_on_release.yml)## Contributing
Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.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: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](https://brooklyn-data.github.io/dbt_artifacts/#!/overview) for documentation on each model.
## Quickstart
1. Add this package to your `packages.yml`:
```
packages:
- package: brooklyn-data/dbt_artifacts
version: 2.6.3
```: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`
```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:
```yml
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:
```yml
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](https://docs.getdbt.com/docs/building-a-dbt-project/package-management#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_reasonTo 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.
```yml
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`.
```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:
```yml
# dbt_project.ymlmodels:
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
- testsThis 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:
```yml
vars:
dbt_artifacts_database: your_db
dbt_artifacts_schema: your_schema
```You must now move these to the following model configs:
```yml
models:
...
dbt_artifacts:
sources:
+database: your_db
+schema: your_schema
```That's because the raw tables are now managed as dbt models. Be aware of any impact that [generate_database_name](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-databases#generate_database_name) and [generate_schema_name](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas#how-does-dbt-generate-a-models-schema-name) macros may have on the final database/schema.
## Migrating From <1.0.0 to >=1.0.0
To migrate your existing data from the `dbt-artifacts` versions <=0.8.0, a helper macro and guide is provided. This migration uses the old `fct_*` and `dim_*` models' data to populate the new sources. The steps to use the macro are as follows:
1. If not already completed, run `dbt run-operation create_dbt_artifacts_tables` to make your source tables.
2. Run `dbt run-operation migrate_from_v0_to_v1 --args ''`.
3. Verify that the migration completes successfully.
4. Manually delete any database objects (sources, staging models, tables/views) from the previous `dbt-artifacts` version.The arguments for `migrate_from_v0_to_v1` are as follows:
| argument | description |
|-------------- |---------------------------------------------------------- |
| `old_database` | the database of the <1.0.0 output (`fct_`/`dim_`) models |
| `old_schema` | the schema of the <1.0.0 output (`fct_`/`dim_`) models |
| `new_database` | the target database that the artifact sources are in |
| `new_schema` | the target schema that the artifact sources are in |The old and new database/schemas *do not* have to be different, but it is explicitly defined for flexible support.
An example operation is as follows:
```bash
dbt run-operation migrate_from_v0_to_v1 --args '{old_database: analytics, old_schema: dbt_artifacts, new_database: analytics, new_schema: artifact_sources}'
```## Acknowledgements
Thank you to [Tails.com](https://tails.com/gb/careers/) for initial development and maintenance of this package. On 2021/12/20, the repository was transferred from the Tails.com GitHub organization to Brooklyn Data Co.
The macros in the early versions package were adapted from code shared by [Kevin Chan](https://github.com/KevinC-wk) and [Jonathan Talmi](https://github.com/jtalmi) of [Snaptravel](snaptravel.com).
Thank you for sharing your work with the community!