Integrate the Databricks API with the Python API

Setup the Databricks API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Databricks and Python remarkably fast. Free for developers.

Popular Databricks and Python Actions#

Get Run Output with the Databricks API

Retrieve the output and metadata of a single task run. See the documentation

Try it

Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

Try it

List Runs with the Databricks API

Lists all runs available to the user. See the documentation

Try it

Run Job Now with the Databricks API

Run a job now and return the id of the triggered run. See the documentation

Try it

Overview of Databricks#

The Databricks API allows you to interact programmatically with Databricks services, enabling you to manage clusters, jobs, notebooks, and other resources within Databricks environments. Through Pipedream, you can leverage these APIs to create powerful automations and integrate with other apps for enhanced data processing, transformation, and analytics workflows. This unlocks possibilities like automating cluster management, dynamically running jobs based on external triggers, and orchestrating complex data pipelines with ease.

Connect Databricks#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    databricks: {
      type: "app",
      app: "databricks",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://${this.databricks.$auth.domain}.cloud.databricks.com/api/2.0/clusters/list`,
      headers: {
        Authorization: `Bearer ${this.databricks.$auth.access_token}`,
      },
    })
  },
})

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}}