Google Cloud

The Google Cloud Platform, including BigQuery

Integrate the Google Cloud API with the Python API

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

Run Python Code with Python API on New Pub/Sub Messages from Google Cloud API
Google Cloud + Python
 
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Run Python Code with Python API on BigQuery - New Row from Google Cloud API
Google Cloud + Python
 
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Run Python Code with Python API on BigQuery - Query Results from Google Cloud API
Google Cloud + Python
 
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New Pub/Sub Messages from the Google Cloud API

Emit new Pub/Sub topic in your GCP account. Messages published to this topic are emitted from the Pipedream source.

 
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BigQuery - New Row from the Google Cloud API

Emit new events when a new row is added to a table

 
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BigQuery - Query Results from the Google Cloud API

Emit new events with the results of an arbitrary query

 
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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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Bigquery Insert Rows with the Google Cloud API

Inserts rows into a BigQuery table. See the docs and for an example here.

 
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Create Bucket with the Google Cloud API

Creates a bucket on Google Cloud Storage See the docs

 
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Create Scheduled Query with the Google Cloud API

Creates a scheduled query in Google Cloud. See the documentation

 
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Get Bucket Metadata with the Google Cloud API

Gets Google Cloud Storage bucket metadata. See the docs.

 
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Overview of Google Cloud

The Google Cloud API opens a world of possibilities for enhancing cloud operations and automating tasks. It empowers you to manage, scale, and fine-tune various services within the Google Cloud Platform (GCP) programmatically. With Pipedream, you can harness this power to create intricate workflows, trigger cloud functions based on events from other apps, manage resources, and analyze data, all in a serverless environment. The ability to interconnect GCP services with numerous other apps enriches automation, making it easier to synchronize data, streamline development workflows, and deploy applications efficiently.

Connect Google Cloud

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module.exports = defineComponent({
  props: {
    google_cloud: {
      type: "app",
      app: "google_cloud",
    }
  },
  async run({steps, $}) {
    // Required workaround to get the @google-cloud/storage package
    // working correctly on Pipedream
    require("@dylburger/umask")()
    
    const { Storage } = require('@google-cloud/storage')
    
    const key = JSON.parse(this.google_cloud.$auth.key_json)
     
    // Creates a client from a Google service account key.
    // See https://cloud.google.com/nodejs/docs/reference/storage/1.6.x/global#ClientConfig
    const storage = new Storage({
      projectId: key.project_id,
      credentials: {
        client_email: key.client_email,
        private_key: key.private_key,
      }
    })
    
    // Uncomment this section and rename for your bucket before running this code
    // const bucketName = 'pipedream-test-bucket';
    
    await storage.createBucket(bucketName)
    console.log(`Bucket ${bucketName} created.`)
  },
})

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}}