dbt Cloud is supported via dedicated components. You can find them in the menu section Components:
kds-team.ex-dbt-cloud-api
for extracting data from dbt Cloud API
kds-team.app-dbt-cloud-job-trigger
for triggering a dbt Cloud job remotely, and optionally, wait for the job results. In that case, the component stores artifacts as well.
The component configuration is pretty straightforward. You must authorize the component by providing your Account ID
, Job ID
, and API key
.
The component generates a status table called dbt_cloud_trigger
storing the job trigger API response:
When Wait for result is selected, the component polls the status until the job ends. The component has a default wait time limit that can be optionally set to a different time. When the option Wait for result is used, the component extracts artifacts, stores them in the file storage, and additionally, produces a job result API call table:
Both tables can be found in the storage, or accessed directly from the job result:
Artifacts can be found in the storage - files - search by tag:
Search by tag (component type or configuration ID):
tags:"componentId-kds-team.app-dbt-cloud-job-trigger"
Tip:: Those files can be also easily retrieved externally via the API or from an integrated Jupyter workspace for further analysis.
Note: Please keep in mind that the base URL of the API call depends on the stack you are using: US vs. Azure EU vs. EU central.
The purpose of this data source connector is to extract and store the dbt Cloud API information (data is stored incrementally) for the following endpoints:
accounts
projects
jobs
runs
run_artifacts
To configure the source connector, enter the API token and select a default configuration:
You can access the data from Storage, or directly from the job detail screen:
Note: The data source connector utilizes our powerful Generic Extractor. In case you want to customize the extraction, select just some endpoints, etc. You can switch to the JSON schema and edit the configuration manually.
© 2024 Keboola