Integrate the Rockset API with the Python API

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

Popular Rockset and Python Actions#

Add Documents with the Rockset API

Add documents to a collection in Rockset. Learn more at https://docs.rockset.com/rest/#adddocuments.

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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.

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Create API Key with the Rockset API

Create a new API key for the authenticated user.

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Create Integration with the Rockset API

Create a new integration with Rockset. Learn more at https://docs.rockset.com/rest/#createintegration

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Overview of Rockset#

Rockset is a real-time indexing database service designed for low-latency, high-concurrency analytics. With the Rockset API, you can query your datasets, create and manage collections, and integrate with event streams for real-time analytics. Using Pipedream's serverless platform, you can automate workflows that react to database events, sync data across services, or trigger actions based on analytical insights.

Connect Rockset#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    rockset: {
      type: "app",
      app: "rockset",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.rs2.usw2.rockset.com/v1/orgs/self/users/self`,
      headers: {
        "Authorization": `ApiKey ${this.rockset.$auth.apikey}`,
      },
    })
  },
})

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}}