Sync data from Google Analytics to Neo4j

CloudQuery is the simple, fast data integration platform that can fetch your data from Google Analytics APIs and load it into Neo4j

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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.

Coming soon for Google Analytics to Neo4j

Fast and reliable

CloudQuery’s efficient design means our syncs are fast and a sync from Google Analytics to Neo4j can be completed in a fraction of the time compared to other tools.

Easy to use, easy to maintain

Google Analytics 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

Neo4j isn’t the only place we can sync your Google Analytics data to. Whatever you need to do with your Google Analytics 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 Google Analytics to Neo4j

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 Google Analytics to Neo4j:

cloudquery init --source=googleanalytics --destination=neo4j

This will generate a config file named googleanalytics_to_neo4j.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 googleanalytics_to_neo4j.yaml

This will start syncing data from the Google Analytics API to your Neo4j 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 is the Neo4j Destination integration for CloudQuery?

The Neo4j Destination integration allows you to send cloud asset data collected by CloudQuery into a Neo4j graph database, enabling you to model and analyze relationships between cloud resources in a highly visual and interconnected format.

How can I use the Neo4j Destination integration for cloud asset inventory management?

By integrating CloudQuery with Neo4j, you can visualize and explore relationships between cloud assets, such as how virtual machines, networks, and storage interact, making it easier to manage your cloud asset inventory.

Is the Neo4j integration suitable for large-scale multi-cloud environments?

Yes, the Neo4j integration is designed to handle large datasets and complex cloud environments, making it a great tool for analyzing and managing assets across multi-cloud deployments.