dbt Cloud
dbt is an intuitive, collaborative platform that lets you reliably transform data using SQL and Python code.
Integrate the dbt Cloud API with the Python API
Setup the dbt Cloud API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate dbt Cloud and Python remarkably fast. Free for developers.
Popular dbt Cloud and Python Triggers#
Popular dbt Cloud and Python Actions#
Retrieve information about an environment. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Retrieve information about a run artifact. See the documentation
Trigger a specified job to begin running. See the documentation
Overview of dbt Cloud#
The dbt Cloud API allows users to initiate jobs, check on their status, and interact with dbt Cloud programmatically. On Pipedream, you can harness this functionality to automate workflows, such as triggering dbt runs, monitoring your data transformation jobs, and integrating dbt Cloud with other data services. By leveraging Pipedream's serverless platform, you can create custom workflows that act on dbt Cloud events or use the dbt Cloud API to manage your data transformation processes seamlessly.
Connect dbt Cloud#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
dbt: {
type: "app",
app: "dbt",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://${this.dbt.$auth.region}.com/api/v3/accounts/`,
headers: {
"Authorization": `Token ${this.dbt.$auth.api_key}`,
"Accept": `application/json`,
},
})
},
})
Overview of Python#
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
Connect Python#
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}