CloudQuery

Data integration and movement is an ever-evolving landscape with many players and exciting developments in recent years. Choosing the right tool depends on the requirements, needs, and resources available. This blog will compare the pros and cons of CloudQuery vs Airbyte to help you make the best decision.

CloudQuery is an open-source, cross-language, high-performance ELT (Extract-Load-Transform) framework powered by Apache Arrow. It is extremely fast and easy to run both locally and in the cloud (either via cloud.CloudQuery.io or self-hosted), as it has a CLI-first design, is shipped as a single binary, and doesn’t need any additional services or UI to run.

Airbyte is an open source data-integration solution. Airbyte both has its own Python SDK to develop connectors as well as a UI and orchestrator to run connectors.

One key difference between the two vendors is architecture and deployment. CloudQuery is CLI first shipped as a single binary and with plugin-based architecture where each plugin is a single binary as well. You run syncs by using configuration-as-code approach and define it in a simple YAML file. CloudQuery Platform adds the UI layer and orchestration.

Airbyte is a UI-first approach where orchestration, configuration, and ELT engine are all coupled together.

Sources and destinations are the bread and butter of data integration solutions. With key differences, pros and cons, for each platform.

CloudQuery’s top connectors are high-performance connectors for AWS, GCP, and Azure that can sync all metadata and configuration from thousands of APIs concurrently to any destination. This massively helps platform engineers create an up-to-date infrastructure lake and drive use cases such as asset inventory, compliance, cost, and others.

Building custom connectors for CloudQuery is quick and can be done in under 15 minutes, with SDKs and tutorials available for the most popular (by usage) programming languages. Airbyte’s own documentation shows that doing the same would take hours and is only possible in either Python or their custom YAML-based DSL.

CloudQuery official source and destination plugins are written in Golang and takes advantage of the excellent Golang Goroutines which can launch huge number of concurrent API calls at with minimal memory footprint. This gives a huge boost to complex connectors like AWS/GCP/Azure where thousands of APIs exist and performance and data freshness are key.

Both solutions use volume-based pricing that varies per container, but CloudQuery also offers a free quota for each connector and even offers annual flat-fee volume quotes to spread the cost of usage spikes over the year, giving you predictable monthly costs.

We’re obviously biased, but we think CloudQuery is the clear winner in flexibility, performance, and pricing.

At the time of writing, Airbyte does have more connectors, but building new or custom connectors to work with CloudQuery takes minutes - and you’re more likely to hit a source API limit before you get a performance bottleneck.

Ready to get started with CloudQuery? You can try out CloudQuery locally with our quick start guide or explore the CloudQuery Platform (currently in beta) for a more scalable solution.

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.