Learn how CloudQuery and Jitsu differ in their key features, development activity, technology stack and community adoption, so you can decide which of these etl & data integration tools is best for you.
Last 30 days
Last commit
2 hours agoRepository age
6 yearsVersion
v6.4.2License
MPL-2.0Repository
cloudquery
Last 30 days
Last commit
5 hours agoRepository age
6 yearsVersion
1.11.0License
MITSelf-hosted
YesRepository
jitsucom/jitsu
Jitsu appears to have several advantages over CloudQuery, particularly in growth, licensing and features. Consider your specific needs regarding popularity, growth, activity, technology, maturity, licensing and features when making your decision.
Both tools have similar popularity levels, with CloudQuery having 6,547 stars and Jitsu having 5,122 stars on GitHub. In terms of developer contributions, CloudQuery has 567 forks, indicating moderate developer engagement.
Jitsu is growing faster, adding 28 stars in the last 30 days (+0.5%) against adding 15 stars for CloudQuery (+0.2%). Jitsu is the smaller project of the two, so it is closing the gap rather than extending a lead.
Both projects show recent activity, with CloudQuery last updated 2 hours ago and Jitsu 5 hours ago.
Both tools share common technology foundations, being built with JavaScript, Bash, Typescript. However, they differ in their additional technology choices: CloudQuery uses Python, Golang, Java while Jitsu leverages CSS, JSX, Next.js.
Both projects started around the same time, with CloudQuery beginning 6 years ago and Jitsu 6 years ago.
Jitsu uses the MIT license, which is more permissive than CloudQuery's MPL-2.0 license, potentially offering greater flexibility for commercial use and integration.
Both tools serve similar use cases in ETL & Data Integration.
Jitsu provides self-hosting options for complete data control and customization, while CloudQuery may be primarily cloud-based or require different deployment approaches.