Sumo Logic

Cloud Log Management, Monitoring, SIEM Tools

Integrate the Sumo Logic API with the Python API

Setup the Sumo Logic API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Sumo Logic 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 Sumo Logic

The Sumo Logic API facilitates the automation of your log management and analytics operations, enabling you to programmatically access Sumo Logic's data ingestion and query capabilities. With this API, you can streamline log data collection, conduct analysis, manage users, and set up alerts, all of which can be harnessed to enhance monitoring, security, and compliance procedures within your organization. Leveraging these functions within Pipedream workflows can help in creating dynamic, cross-functional integrations to optimize data-driven decision-making processes.

Connect Sumo Logic

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    sumo_logic: {
      type: "app",
      app: "sumo_logic",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.${this.sumo_logic.$auth.deployment}.sumologic.com/api/v1/users`,
      auth: {
        username: `${this.sumo_logic.$auth.accessId}`,
        password: `${this.sumo_logic.$auth.accessKey}`,
      },
    })
  },
})

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}}