Sync data from Terraform to Google Cloud Storage

CloudQuery is the simple, fast data integration platform that can fetch your data from Terraform APIs and load it into Google Cloud Storage

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Self-hosted

Start locally, then deploy to a Virtual Machine, Kubernetes, or anywhere else. Full instructions on CLI setup are available in our documentation.

Step-by-step Instructions

Cloud-hosted

Start syncing in a few clicks. No need to deploy your own infrastructure.

Fast and reliable

CloudQuery’s efficient design means our syncs are fast and a sync from Terraform to Google Cloud Storage can be completed in a fraction of the time compared to other tools.

Easy to use, easy to maintain

Terraform syncing using CloudQuery is easy to set up and maintain thanks to its simple YAML configuration. Once synced, you can use normal SQL queries to work with your data.

A huge library of supported destinations

Google Cloud Storage isn’t the only place we can sync your Terraform data to. Whatever you need to do with your Terraform data, CloudQuery can make it happen. We support a huge range of destinations, customizable transformations for ETL, and we regularly release new plugins.

Extensible and Open Source SDK

Write your own connectors in any language by utilizing the CloudQuery open source SDK powered by Apache Arrow. Get out-of-the-box scheduling, rate-limiting, transformation, documentation and much more.

Step by step guide for how to export data from Terraform to Google Cloud Storage

MacOS Windows Linux

MacOS Setup

Step 1: Install CloudQuery

To install CloudQuery, run the following command in your terminal:

brew install cloudquery/tap/cloudquery

Step 2: Create a Configuration File

Next, run the following command to initialize a sync configuration file for Terraform to Google Cloud Storage:

cloudquery init --source=terraform --destination=gcs

This will generate a config file named terraform_to_gcs.yaml. Follow the instructions to fill out the necessary fields to authenticate against your own environment.

Next, log in to the CloudQuery CLI. If you have't already, you can sign up for a free account as part of this step:

cloudquery login

Step 4: Run a Sync

cloudquery sync terraform_to_gcs.yaml

This will start syncing data from the Terraform API to your Google Cloud Storage database! 🚀

See the CloudQuery documentation portal for more deployment guides, options and further tips.

FAQs

What is CloudQuery?

CloudQuery is an open-source tool that helps you extract, transform, and load cloud asset data from various sources into databases for security, compliance, and visibility.

Why does CloudQuery require login?

Logging in allows CloudQuery to authenticate your access to the CloudQuery Hub and monitor usage for billing purposes. Data synced with CloudQuery remains private to your environment and is not shared with our servers or any third parties.

What data does CloudQuery have access to?

CloudQuery accesses only the metadata and configurations of your cloud resources that you specify without touching sensitive data or workloads.

How is CloudQuery priced?

CloudQuery offers flexible pricing based on the number of cloud accounts and usage. Visit our pricing page for detailed plans.

Is there a free version of CloudQuery?

Yes, CloudQuery offers a free plan that includes basic features, perfect for smaller teams or personal use. More details can be found on our pricing page.

What file formats can CloudQuery load from Terraform to Google Cloud Storage?

CloudQuery can output CSV, JSON and Parquet files to Google Cloud. If you choose a CSV file, there are a number of CSV specific parameters that you can set before running the sync such as the delimiter and whether the header row is skipped.

How do I authenticate CloudQuery with my Google Cloud Storage environment?

The CloudQuery GCS integration authenticates by using your Application Default Credentials and all of the options are available. For testing purposes, it is possible to specify your GCS access credentials via the GOOGLE_APPLICATION_CREDENTIALS parameter but this is not recommended for long-term usage.