How to Visualize CloudQuery Data with Google Data Studio

Itay Zagron
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Itay Zagron
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In this guide, we will walk you through how to set up CloudQuery to build your cloud asset inventory in PostgreSQL and connect it to Google Data Studio (opens in a new tab) for visualization, monitoring and reporting.

General Architecture

What you will get

  • Raw SQL access to all your cloud asset inventory to create views or explore any questions or connection between resources.
  • Multi-Cloud Asset Inventory: Ingest configuration from all your clouds to a single datastore with a unified structure.
  • Avoid yet-another-dashboard fatigue: Reuse your existing Google Data Studio setup to build a cloud asset inventory.

Walkthrough

Step 1: Install or Deploy CloudQuery

If it’s your first time using CloudQuery we suggest you first run it locally to get familiar with the tool, take a look at our quickstart guide and GCP source plugin (opens in a new tab).

If you are already familiar with CloudQuery, take a look at how to deploy it to GCP on Cloud SQL and GKE at https://github.com/cloudquery/terraform-gcp-cloudquery (opens in a new tab).

Step 2: Connecting Google Data Studio to PostgreSQL

You can only connect Data Studio to a public PostgreSQL (GCP Cloud SQL).

For security purpose you should allow connection only from Google Data Studio IP Addresses (opens in a new tab).

See connection full walkthrough (opens in a new tab).

Click Create New datasource and choose PostgresSQL (In this tutorial we will connect to publicly accessible RDS with authorized Data Studio IP Address) and fill-in the connection details:

Step 3: Visualize the Data!

Choose the table you want to visualize, in this case we will choose the gcp_resources view.

💡 To create the gcp_resources view, run the following view (opens in a new tab) before importing to the data studio.

Choose the table to visualize

Design your report

You can reuse Data Studio to export/share those reports as well!

Summary

In this post we showed you how to build an open-source cloud asset inventory with CloudQuery as the ETL (Extract-Transform-Load) / data-ingestion layer and Google Data Studio as the visualization platforms. This approach eliminates the yet-another-dashboard fatigue and gives you the ability to pick the best-in-class visualization tools and/or reuse your current stack.