Confluent

The platform to set data in motion.

Integrate the Confluent API with the Python API

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

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

Overview of Confluent

The Confluent API provides programmatic interaction with Confluent Cloud, a fully managed Kafka service. It lets you manage Kafka clusters, topics, users, and configurations, enabling seamless integration and data streaming capabilities. With Pipedream, you can create workflows that automate interactions with your Kafka infrastructure, such as triggering events on message arrival, managing topics, or integrating Kafka data with other services.

Connect Confluent

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    confluent: {
      type: "app",
      app: "confluent",
    }
  },
  async run({steps, $}) {
    const data = {
      "topic_name": `pipedream-test`,
    }
    return await axios($, {
      method: "post",
      url: `https://${this.confluent.$auth.rest_endpoint_url}/kafka/v3/clusters/${this.confluent.$auth.cluster_id}/topics`,
      headers: {
        "Content-Type": `application/json`,
      },
      auth: {
        username: `${this.confluent.$auth.api_key}`,
        password: `${this.confluent.$auth.api_secret}`,
      },
      data,
    })
  },
})

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

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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}}