Learn how Deepnote and Lightdash differ in their key features, development activity, technology stack and community adoption, so you can decide which of these bi platforms is best for you.
Last 30 days
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Repository age
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Last 30 days
Last commit
Repository age
Version
License
Self-hosted
Repository

Lightdash appears to have several advantages over Deepnote, particularly in popularity, growth, maturity, licensing and features. Consider your specific needs regarding popularity, growth, activity, technology, maturity, licensing and features when making your decision.
Lightdash leads in popularity with 6,124 stars vs 3,004 stars for Deepnote. The 104% higher star count indicates stronger community adoption. In terms of developer contributions, Lightdash has 772 forks, indicating moderate developer engagement.
Lightdash is growing faster, adding 101 stars in the last 30 days (+1.7%) against adding 8 stars for Deepnote (+0.3%). Lightdash is both larger and pulling further ahead.
Both projects show recent activity, with Deepnote last updated 13 hours ago and Lightdash 12 hours ago.
Both tools share common technology foundations, being built with JavaScript, Typescript. However, they differ in their additional technology choices: Deepnote uses Python while Lightdash leverages CSS, Bash, JSX, Next.js.
Lightdash has been in development longer, starting 5 years ago, compared to Deepnote which began 11 months ago. This 4.6-year head start suggests Lightdash may have more mature features and established processes.
Lightdash uses the MIT license, which is more permissive than Deepnote's Apache-2.0 license, potentially offering greater flexibility for commercial use and integration.
Both tools serve similar use cases in BI Platforms. However, they also have distinct specializations: Deepnote also focuses on Data Platforms for AI while Lightdash extends into Data Visualization, Semantic Layer Platforms.
Lightdash provides self-hosting options for complete data control and customization, while Deepnote may be primarily cloud-based or require different deployment approaches.