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!

The package currently supports Databricks, Spark and Snowflake adapters.

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: 1.1.2

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. Create the tables dbt_artifacts uploads to with dbt run-operation create_dbt_artifacts_tables

5. Run your project!

Configuration

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

vars:
  dbt_artifacts_database: your_db # optional, default is your target database
  dbt_artifacts_schema: your_schema # optional, default is your target schema

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

Note that the model materializations are defined in this package's dbt_project.yml, so do not set them in your project.

Environment Variables

If the project is running in dbt Cloud, the following five columns (https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-environment-variables#special-environment-variables) 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',
    '...'
  ]

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 '<see-below-for-arguments>'.
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:

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 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 and Jonathan Talmi of Snaptravel.

Thank you for sharing your work with the community!

Contributing

Running the tests

1. Install pipx

pip install pipx
pipx ensurepath

2. Install tox

pipx install tox

3. Copy and paste the integration_test_project/example-env.sh file and save as env.sh. Fill in the missing values.

4. Source the file in your current shell context with the command: . ./env.sh.

5. From this directory, run

tox -e integration_snowflake # For the Snowflake tests
tox -e integration_databricks # For the Databricks tests

The Spark tests require installing the ODBC driver. On a Mac, DBT_ENV_SPARK_DRIVER_PATH should be set to /Library/simba/spark/lib/libsparkodbc_sbu.dylib. Spark tests have not yet been added to the integration tests.

SQLFluff

We use SQLFluff to keep SQL style consistent. A GitHub action automatically tests pull requests and adds annotations where there are failures. SQLFluff can also be run locally with tox. To install tox, we recommend using pipx.

Install pipx:

pip install pipx
pipx ensurepath

Install tox:

pipx install tox

Lint all models in the /models directory:

tox

Fix all models in the /models directory:

tox -e fix_all

Lint (or subsitute lint to fix) a specific model:

tox -e lint -- models/path/to/model.sql

Lint (or subsitute lint to fix) a specific directory:

tox -e lint -- models/path/to/directory

Rules

Enforced rules are defined within tox.ini. To view the full list of available rules and their configuration, see the SQLFluff documentation.