Cloud monitoring as a service
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
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.
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}`,
},
})
},
})
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:
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}}