How to Visualize CloudQuery Data with Google Data Studio

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 for visualization, monitoring and reporting.

General Architecture

• ETL (Extract-Transform-Load) ingestion layer: CloudQuery
• Datastore: PostgreSQL
• Data Visualization and Exploration Platform: Google Data Studio

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 integration.

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.

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.

See connection full walkthrough.

Click Create New datasource and choose PostgreSQL (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 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.

Want to see CloudQuery in action? Schedule a demo with our team or check out the platform documentation to learn more.

Want help getting started? Join the CloudQuery community to connect with other users and experts, or message our team directly here if you have any questions.