Table of contents
Collaborative Features
Deepnote allows for real-time collaboration, similar to Google Docs, where multiple users can work on the same notebook simultaneously without conflicts.
Integration with Popular Tools
Deepnote integrates seamlessly with popular data sources and tools such as Google Drive, GitHub, and SQL databases, enhancing its versatility for data science projects.
User-Friendly Interface
The interface is clean and easy to navigate, making it accessible for both beginners and experienced data scientists.
Cloud-Based
Being a cloud-based solution, Deepnote eliminates the need for local setup and maintenance, allowing users to access their projects from anywhere with internet access.
Data Security
Deepnote provides robust security features, ensuring that your data and notebooks are protected against unauthorized access.
Integrated Version Control
Version control within Deepnote allows users to track changes, revert to previous versions, and collaborate more effectively on shared projects.
Deepnote is an excellent tool for data scientists, particularly those who value collaboration and need interactive, shareable notebooks. Its user-friendly interface and powerful integration capabilities make it a strong contender in the data science notebook space.
We have collected here some useful links to help you find out if Deepnote is good.
Check the traffic stats of Deepnote on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Deepnote on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Deepnote's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Deepnote on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Deepnote on Reddit. This can help you find out how popualr the product is and what people think about it.
Thank you for the list - I think I've come across all of these in my research! I'll try highlight the differences for each. - https://noteable.io/ - as you say, it doesn't exist anymore - https://deepnote.com - I actually mentioned this in the post but in my experience, the UX and features far behind what we've built already. I'd love to hear from anyone who's tried jupyter-ai to give us a shot and let me know... - Source: Hacker News / 12 months ago
- https://deepnote.com -- also extensive AI integration and realtime collaboration. - Source: Hacker News / 12 months ago
Deepnote - A new data science notebook. Jupyter is compatible with real-time collaboration and running in the cloud. The free tier includes unlimited personal projects, up to 750 hours of standard hardware, and teams with up to 3 editors. - Source: dev.to / over 1 year ago
We looked into many of these issues with Deepnote (YC S19) [https://deepnote.com/]. What we found is that these are not necessarily problems of the underlying medium (a notebook), but more of the specific implementation (Jupyter). We've seen a lot of progress in the Jupyter ecosystem, but unfortunately almost none in the areas you mentioned. - Source: Hacker News / about 2 years ago
Upload your ipynb to Deepnote and publish as an app. That simple. https://deepnote.com. - Source: Hacker News / about 2 years ago
Using Deepnote, we'll create a Python notebook and upload the two GeoJSON files into a data directory. - Source: dev.to / over 2 years ago
Deepnote - A new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud. Free tier includes unlimited personal projects, up to 750 hours of standard hardware and teams with up to 3 editors. - Source: dev.to / over 2 years ago
I think your site looks good and I have used the type of service you offer, but there are 2 potential problems. As SheepherderPatient51 said,Google already offers all of this for free (and so does https://kaggle.com and https://www.paperspace.com ). There are also other sites just like yours such as https://deepnote.com,https://saturncloud.io, and https://lambdalabs.com . Source: over 2 years ago
Check out deepnote. They have an actually free tier, with which you and two other people can be on the same notebook at once. It’s like google docs but for notebooks. I’ve used it to teach data science courses, and it’s been really helpful. You can also change your compute if needed. Integration with plenty of services included. Source: over 2 years ago
One way is to offload this to the cloud, with deepnote.com being one of the cloud options. Source: almost 3 years ago
Deepnote (YC S19) is working on this (I'm the founder). https://deepnote.com/. - Source: Hacker News / almost 3 years ago
Https://deepnote.com is exactly this! Disclaimer: I work for Deepnote. - Source: Hacker News / about 3 years ago
I wrote this article. For some background— In March, we published an article on Stock Trades by members of congressional committees: https://innerjoin.bit.io/data-cant-tell-us-whether-congressional-stock-trading-is-corrupt-b32031c533bc To conduct this research, we needed to know: (1) which members of congress made which stock trades, and (2) which members of congress belonged to which congressional committees. The... - Source: Hacker News / about 3 years ago
I am considering Hex, Deepnote, and possibly Databricks. Does anyone have any experience using the first 2 (i have worked with Databricks in the past) and have thoughts they can share? The company isn't doing any fancy data science so far so I mostly want it for deep product analytics which I can turn into reports that are easily shareable across the org. That being said, I do want to get into statistical... Source: about 3 years ago
Deepnote, a California-based company, solves this problem by offering a collaborative notebook tool that allows teams to explore, analyze, and display data from beginning to end together. Deepnote enables users to share projects with others via just a link. Setting team permissions allows the user to control who can edit code and who can view it. It also has capabilities like adding comments and checking the... Source: over 3 years ago
You wont be doing Machine Learning in Excel, so my best guess its just a bunch of fancy logic and scripts that need to be yoinked out and re-coded as SQL or python code. If you can separate your data layer and logic layer, you can store your data in BigQuery, and hook up Data Studio as u/Captain_Flashheart mentions to give your team a frontend interface to view the data. You can then implement your logic layer as... Source: over 3 years ago
I also want to give a huge shoutout to Deepnote (https://deepnote.com) for sponsoring this video. Source: over 3 years ago
Deepnote notebooks have univariate summaries similar to the ones you referred to & you can also filter the data frames and sort them without any extra code. Source: over 3 years ago
How is this different from [1] deepnote? I have been using them for quite a while and it is a joy collaborating on notebooks real-time. [1] https://deepnote.com/. - Source: Hacker News / over 3 years ago
Keep in mind also that you can code together with multiple people in real time using things like DeepNote. Source: almost 4 years ago
Deepnote - A new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud. Free tier includes unlimited personal projects, up to 750 hours of standard hardware and teams with up to 3 editors. - Source: dev.to / almost 4 years ago
Based on the context provided and recent mentions of Deepnote, it becomes evident that Deepnote continues to build its reputation in the data science and machine learning sectors as a robust, cloud-based data science notebook. Deepnote is often compared with Jupyter Notebooks but with enhanced features, primarily focusing on real-time collaboration and integration with several popular data sources. This aspect of real-time collaboration is frequently highlighted across various mentions, showcasing it as a significant advantage over traditional notebook interfaces.
Key Features and User Preferences:
Deepnote's real-time collaborative environment is akin to Google Docs but tailored for data notebooks, allowing multiple users to work on the same project synchronously. Users benefit from the platform's intuitive and modern interface, which is reported to appeal to both novice and experienced data scientists. The smart autocomplete, version control, and code review functionality further enhance the coding environment, making it a favorable choice for data-driven projects.
The seamless integration with data sources such as GitHub, Google Drive, BigQuery, and PostgreSQL underscores its utility in a modern data workflow, enabling easy data access and manipulation. The ease of publishing notebooks as applications is also noted, adding a layer of usability for users who aim to share interactive data analyses.
Public Opinion and Competitor Analysis:
Despite its strengths, some users critique Deepnote, indicating that its features are lagging behind some competitors' offerings, particularly in scenarios requiring more extensive AI model support. However, the software's focus on collaboration, integration, and unique features like smart autocomplete is generally recognized and lauded.
Competitors such as Amazon SageMaker and Databricks often attract users needing more advanced machine learning infrastructure, especially where GPU support is a criterion—a feature still awaited in Deepnote. Some users prefer other platforms like Kaggle for free access to resources, particularly in contexts where cloud-based GPU resources are critical.
Deepnote's free tier is also a notable aspect appreciated by its users, providing a viable entry point for individuals and small teams who need robust data science tools without immediate financial commitments. This tier includes personal projects and standard hardware resources, making it conducive for learners and educators.
Conclusion:
In summary, Deepnote emerges as a compelling option for collaborative data science workflows, with its strengths in real-time editing and seamless integrations gaining positive feedback. While it may not yet appeal to all niche users, particularly those requiring extensive ML infrastructure, its continuous development and attention to collaborative functionalities place it among the noteworthy alternatives to traditional data notebook solutions like Jupyter. Future updates, particularly regarding GPU support and enhanced AI features, will likely influence further adoption and competitive positioning.
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