What is the main difference between DataLens and Deepnote?

DataLens and Deepnote are both bi platforms but differ in their approach, features, and target audience. Compare their GitHub stats, technology stack, and community adoption to make the best choice.

Which is better: DataLens or Deepnote?

The choice between DataLens and Deepnote depends on your specific needs, technical requirements, and team preferences. Both are quality open source solutions with active communities.

Can I migrate from DataLens to Deepnote?

Migration between DataLens and Deepnote is possible but requires careful planning. Consider data export/import capabilities, feature parity, and potential workflow changes.

Stellar Hosted – Managed Open Source software hosting in the EU: secure, compliant, fast.

Learn More

Learn more

DataLens vs Deepnote

Learn how DataLens and Deepnote 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.

DataLens

Modern analytics system featuring user-friendly interface, native integrations, and unlimited scalability. Build, visualize, and share data insights across your organization.

Visit DataLens Read more

Stars
1,691
Forks
115
Last commit
1 month ago
Repository age
3 years
License
Apache-2.0
Self-hosted
Yes

Activity score

78/100

View Repository

Deepnote

Deepnote is an open-source collaborative notebook for data analysts and scientists, combining Python, SQL, AI assistance, and live sharing in one cloud platform.

Visit Deepnote Read more

Stars
2,953
Forks
199
Last commit
18 hours ago
Repository age
10 months
License
Apache-2.0

Activity score

87/100

View Repository

Detailed Comparison

Both DataLens and Deepnote have their unique strengths and serve similar purposes effectively. Consider your specific needs regarding popularity, activity, technology, maturity, licensing and features when making your decision.

Deepnote wins

Community & Popularity

Deepnote leads in popularity with 2,953 stars vs 1,691 stars for DataLens. The 75% higher star count indicates stronger community adoption. In terms of developer contributions, Deepnote has 199 forks, indicating moderate developer engagement.

Deepnote wins

Development Activity

Deepnote shows more recent development activity with its last commit 18 hours ago, while DataLens was last updated 1 month ago. This suggests Deepnote is being more actively maintained.

Comparable

Technology Stack

Both tools share common technology foundations, being built with JavaScript, Python. However, they differ in their additional technology choices: DataLens uses Bash while Deepnote leverages Typescript.

DataLens wins

Project Maturity

DataLens has been in development longer, starting 3 years ago, compared to Deepnote which began 10 months ago. This 2.1-year head start suggests DataLens may have more mature features and established processes.

Comparable

Licensing

Both projects use the Apache-2.0 license, providing identical terms for usage and distribution.

Comparable

Use Cases & Features

Both tools serve similar use cases in BI Platforms. However, they also have distinct specializations: DataLens also focuses on Data Visualization while Deepnote extends into Data Platforms for AI.

DataLens wins

Hosting & Deployment

DataLens provides self-hosting options for complete data control and customization, while Deepnote may be primarily cloud-based or require different deployment approaches.