Deepnote is a data science notebook and collaboration platform that allows data scientists to easily create, run, and share analysis using real-time collaboration tools. Deepnote integrates Python, R, SQL and other data tools into one easy browser-based environment.
Deepnote: Data Science Notebook & Collaboration Platform
Deepnote is a data science notebook and collaboration platform that allows data scientists to easily create, run, and share analysis using real-time collaboration tools. Deepnote integrates Python, R, SQL and other data tools into one easy browser-based environment.
What is Deepnote?
Deepnote is a cloud-based data science notebook platform purpose-built for teams. Launched in 2018, Deepnote allows data scientists, analysts, engineers and business users to easily create, run, and share analysis using real-time collaboration tools.
Key features of Deepnote include:
Integrated Python, R, SQL and other development environments - no setup required
Real-time collaborative editing - work together on the same analysis files and notebooks
Everything tracked - full version control, commenting and roll-back built-in
Shareable and publishing - create internal dashboards pages or publicly publish results as javascript-powered apps
Security and access control - control permissions at project, environment and file levels
Deepnote is designed to improve the productivity of both individual data scientists by eliminating environment setup and maintenance, while facilitating better collaboration through built-in real-time co-editing features. The platform is especially popular with data teams at fast-growing startups and mid-market companies.
Unlike some other notebook platforms that focus only on individuals, Deepnote was purpose-built with collaboration in mind. As data science teams scale, tools that allow easy sharing of work, commenting and task management become critical.
Deepnote Features
Features
Real-time collaboration
Integrated Python, R, SQL and other data science tools
Version control integration
Shareable and publishable notebooks
Cloud-based platform
Supports large datasets and GPUs
Pricing
Freemium
Pros
Collaborate in real-time on notebooks
No setup required
Integrates well with popular data science tools
Easy to share and publish analyses
Scales to large datasets and computations
Cons
Limited customization compared to Jupyter
Less flexibility than local development environments
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