Deepnote Reviews & Product Details

Our data mining and analytics process is made easier because integration with PostgreSQL and Google BigQuery is ultra simple. No complicated configurations are required thanks to their simple integrations. We’ve been able to use Deepnote to build notebooks where we import all the data directly from Google BigQuery, make interactive visualizations and import the data. My team was able to review the data and provided me with immediate feedback after sharing the notebook with them. Review collected by and hosted on G2.com.

It has come to my attention that performance could drop when dealing with really huge data sets, which impacts the agility of analysis. Review collected by and hosted on G2.com.

It's very easy to get started with Deepnote, and the environment is very comfortable if you like notebooks. In many ways it reminds me of JupyterLab but with a modern and enterprise feel. You can have multiple notebooks per project (something competitors don't allow) and it is extremely easy to deploy a dashboard or report with a few clicks.

The AI features are very handy and speed up the low value work of typing syntax correctly. Review collected by and hosted on G2.com.

Data apps could be improved somewhat. It's hard to pinpoint but there are some improvements to be made on the responsiveness of the apps and the overall look. Review collected by and hosted on G2.com.

I love that my team doesn't need to be a python expert in order to take advantage of the benefits of programming efficiencies and insights. My data science team does all of the magic of building the tools, and Deepnote allows my analysts to just point and click to get the insights they need. Could not be easier to use. Review collected by and hosted on G2.com.

You do need to understand how to use python and variables in order to get Deepnote setup, but for most programmers the learning curve is small. Review collected by and hosted on G2.com.

As a university student who was using Deepnote while working on a team data visualization project, I loved how the interface made collaboration seamless. The real-time multi-user editing was a game-changer for us, which allowed us to easily work on different parts of the code remotely. As we were in a group, having the ability to organize our work into our structured notebooks was extremely handy, and kept all of the various components tidy. I found the commenting feature to be particularly helpful for sharing feedback throughout the project, which really helped with improving our efficiency as a team. Overall, I found Deepnote to be extremely user intuitive as a platform, and will definitely continue to use it in projects going forth. Review collected by and hosted on G2.com.

While I found almost every aspect of Deepnote to be perfect, one thing that I did experience was the lag or lengthy loading time for larger projects. Given the nature of our final-year project and the size of datasets we were working with, it sometimes took longer than expected for projects to load. It wasn’t a dealbreaker, but it did slow us down occasionally during crunch times. Review collected by and hosted on G2.com.

Previously used Jupyter Notebook and PyCharm. Would like to say that Deepnote is more convenient, you just create python or SQL cell in a matter of seconds. Making new integrations is also mostly very easy. UI is very straightforward and user-friendly, everything that is often needed in everyday work is by your hand (table schemas, timetables for your script, integrations, etc.) Review collected by and hosted on G2.com.

The only real drawback I see is that sometimes Deepnote stops working or works very slowly. It can disrupt your work, but actually I would like to say that such things happen not more than 2 times a month, moreover, in most cases it is fixed quite quickly, so I have not missed a single deadline in my daily routine. Review collected by and hosted on G2.com.

I have been using Deepnote for more than a year and I'm super satisfied with it. It's easy to connect to our data watehouse and run our first line of code. Deepnote AI makes data exploration way faster, we are power user of the App function. Several applications are used across the organisation. Notebook scheduling is also extremely useful for us with integrating Deepnote to Slack and Notion. We were able to create a fully automated reporting for our stakeholders. Review collected by and hosted on G2.com.

Althought we love the product, there are some areas where the team could make some improvements. Deppnote runs our code on their own machines, not using our AWS sources, so somethimes queries run slower than usual. I'd really like to see a canvas feature so we would be able to create customized reports (i.e. a Metric Tree). Review collected by and hosted on G2.com.

Deepnote has truly transformed the way I work with data. Its user-friendly interface is both intuitive and visually appealing, making it a joy to navigate even the most complex tasks. The platform's speed and efficiency are unmatched, allowing me to focus on insights rather than waiting for processes to complete.

One of the standout features is the sheer versatility in data connectivity options. Whether you're pulling from databases, APIs, or cloud storage, Deepnote seamlessly integrates with a wide array of data sources, providing unparalleled flexibility for any project.

Moreover, their AI and copilot functionalities are second to none. These tools not only enhance productivity but also empower users with intelligent suggestions and automation capabilities that streamline workflows and boost creativity.

In summary, Deepnote is a powerhouse for anyone looking to elevate their data analysis experience. It's a must-have tool for data professionals and enthusiasts alike. Review collected by and hosted on G2.com.

One area where Deepnote could improve is in its flexibility regarding the customization of block positioning within the app. Currently, users have limited control over arranging blocks to suit their workflow preferences. Enhancing this feature would greatly benefit users by allowing for a more personalized and efficient workspace, tailored to individual needs and project requirements. Review collected by and hosted on G2.com.

Really easy to get started and just jump in and go. Lots of database connectors.

The unlimited VMs at the Pro size (4 vcpu, 16 GB) is perfect for everyday work.

The AI integration is the best across all competitors. We tested that integration out across 6 competitors and this is by far the best. It's awareness of the entire notebook's existing codebase, results and next steps is great. Other notebook tools typically struggle with a previous cell's output and you have to be really explicit and repeat code and/or results from previous cells to work with.

The apps feature is great to share content with non-technical users. Review collected by and hosted on G2.com.

There is one competitor that works better with Snowflake by integrating directly with Snowpark by translating python code to run on-warehouse instead of on the VM. If you have large data sets, it's better to run it on the warehouse.

You can just explicitly run Snowpark code in the notebook but it's not "magic" so it doesn't just translate it for you. For me, that's fine. I'd rather run it on the VM anyways and know exactly where my code runs but I could see folks who don't want to write all that extra code. Review collected by and hosted on G2.com.

It's very easy to set up, great for collaboration and inexpensive when compared to other data science tools, not to mention intuitive. I use it to call APIs and capture and send data to our BI tool, and compared to what the tool would charge us to have Python in their platform, Deep note is virtually free. Review collected by and hosted on G2.com.

Deepnote is missing integrations with Azure storage and Google Drive at the moment so that could be an issue. Review collected by and hosted on G2.com.

Deepnote has completely transformed my workflow as a data scientist. It combines the functionality of traditional Jupyter notebooks with powerful collaborative features, making it an essential tool for any data science team. The platform is incredibly intuitive, with a clean interface and built-in support for all major data science libraries.

One of the standout features is real-time collaboration – I can work alongside my teammates just like in Google Docs, which has significantly boosted our productivity. Setting up projects is seamless, and Deepnote handles the back-end configurations, so we can focus entirely on data exploration and analysis.

The integration with various data sources, from databases to APIs, is smooth and easy to manage, saving us so much time on setup. The support team is also highly responsive and genuinely cares about user feedback, frequently rolling out updates that make the platform even better.

Whether you’re a beginner or a seasoned data professional, Deepnote will elevate your projects. Highly recommend it to anyone looking for a reliable, efficient, and collaborative data science environment! Review collected by and hosted on G2.com.

Honestly, there’s very little to dislike about Deepnote! If I had to pick something, it would be great to see even more customization options for themes and layout preferences. But even as it stands, Deepnote’s design and functionality are top-notch. Review collected by and hosted on G2.com.