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 (opens in a new tab) for visualization, monitoring and reporting.
- ETL (Extract-Transform-Load) ingestion layer: CloudQuery (opens in a new tab)
- Datastore: PostgreSQL
- Data Visualization and Exploration Platform: Google Data Studio (opens in a new tab)
- 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.
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).
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:
Choose the table you want to visualize, in this case we will choose the
💡 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!
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.