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.

You can get the information needed to connect to your Turbot Pipes database instance from the Query tab for your workspace. On the Query tab, click the info button at the top of the query window to show the connection information.

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"
    }
  ]
}