Integrate the Datadog API with the Python API

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

Popular Ways to Connect Datadog with Python#

Run Python Code with Python API on New Monitor Event (Instant) from Datadog API

Datadog + Python

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Popular Datadog and Python Triggers#

New Monitor Event (Instant) from the Datadog API

Emit new events captured by a Datadog monitor

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Popular Datadog and Python Actions#

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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Post Metric Data with the Datadog API

The metrics end-point allows you to post time-series data that can be graphed on Datadog's dashboards. See docs

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Overview of Datadog#

The Datadog API, accessible through Pipedream, empowers you to programmatically interact with Datadog's monitoring and analytics platform. This enables developers to automate the retrieval of monitoring data, manage alert configurations, and synchronize service health information across systems. With Pipedream's serverless execution model, you can create intricate workflows that react to Datadog events or metrics, manipulate the data, and pass it on to other services or even Datadog itself for a cohesive operational ecosystem.

Connect Datadog#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    datadog: {
      type: "app",
      app: "datadog",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.datadoghq.com/api/v1/user`,
      headers: {
        "DD-API-KEY": `${this.datadog.$auth.api_key}`,
        "DD-APPLICATION-KEY": `${this.datadog.$auth.application_key}`,
      },
    })
  },
})

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}}