Connect to Turbot Pipes from Jupyter Notebook

Since your Turbot Pipes workspace is just a Postgres database, you can use the standard psycopg2 adapter to query your workspace database from Python.

The Connect tab for your workspace provides the details you need to connect a Jupyter Notebook to Turbot Pipes.

It's the usual drill: import psycopg2, specify your connection string, create a connection, then run a query. (See also: Connect to Turbot Pipes from Python.)

In this example we connect from an instance of Jupyter Notebook running in VSCode, load the query results into a

pandas.DataFrame, then use itsdescribemethod to summarize the data.

Connect to Steampipe CLI from Jupyter Notebook

To connect Jupyter Notebook to Steampipe CLI, run steampipe service start --show-password and use the displayed connection details.

Steampipe service is running:

Database:

  Host(s):            localhost, 127.0.0.1, 172.28.158.171
  Port:               9193
  Database:           steampipe
  User:               steampipe
  Password:           9a**-****-**7e
  Connection string:  postgres://steampipe:9a49-42e2-a57e@localhost:9193/steampipe

Call the Turbot Pipes API from Jupyter Notebook

You can also use the Turbot Pipes query API. Grab your token, put it an environment variable like PIPES_TOKEN, and use this pattern.

import json, os, requests
url = 'https://pipes.turbot.com/api/latest/org/acme/workspace/jon/query'
data = {'sql':'select name, region from aws_s3_bucket limit 2'}
token = os.environ['PIPES_TOKEN']
headers = {"Authorization": "Bearer " + token}
r = requests.post(url, headers=headers, data=data)
print(json.dumps(r.json(),indent=4))

{
  "items": [
    {
      "name": "10k-with-bucket-kms",
      "region": "us-east-2"
    },
    {
      "name": "10k-with-standard-kms",
      "region": "us-east-2"
    }
  ]
}