Deepnote

What do you like best about Deepnote?

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.

What do you dislike about Deepnote?

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.

What problems is Deepnote solving and how is that benefiting you?

The main aspect that I think Deepnote tackles extremely well, that I felt while using the platform for my final-year data visualization project, was its exceptional ease of use for collaborative work. It allowed all of us to work on the same notebook in real time without the risk of versioning issues (which I have personally encountered on other platforms freqeuntly) or overwriting each other's work, which saved us a great deal of time and stress. For a large team, it's organizational features were also highly beneficial, keeping our work structured and easy to navigate, which made team meetings and progress updates far more effective overall. Additionally, the built-in commenting feature enabled us to share feedback directly within the notebook, eliminating the need for us to send code on other communication platforms. Overall, Deepnote allowed us to focus on the project itself rather than the various logistical hurdles, which greatly enhanced my productivity and the quality of teamwork.

What do you like best about Deepnote?

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.)

What do you dislike about Deepnote?

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.

What problems is Deepnote solving and how is that benefiting you?

It is a notebook consisting of different cells of code, which is very useful for working with data via python and SQL. By creating additional cells you can easily control what intermidiate results you want to see in your pipeline, and what results you do not want to see. It is very important if you need to understand reasons of data loss, or reasons of mistakes made in the initial source of data. You can't use PyCharm of VS code for these purposes, or it is less convenient. Jupyter Notebook is also great for this, but another advantage of Deepnote is that integrations and e.g. contents are all easily set or already integrated in your notebook. In Jupyter Notebook there is a lot of extenstions you need to download by yourself, and it takes much more time and effort to do this.

What do you like best about Deepnote?

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.

What do you dislike about Deepnote?

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).

What problems is Deepnote solving and how is that benefiting you?

Easy integration to our data warehouse and supporting collaboration. So we don't need to struggle with connecting to Athena, it's easy to set up as 1-2-3. We are able to collaborate seamlessly on analysis and ML modeling.

What do you like best about Deepnote?

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.

What do you dislike about Deepnote?

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.

What problems is Deepnote solving and how is that benefiting you?

Deepnote has revolutionized the way we analyze product user data. By providing a seamless platform for data exploration and visualization, it empowers our team to dive deep into user behavior and preferences. This has been instrumental in identifying which features resonate most with our audience, allowing us to strategically focus our development efforts.

The intuitive interface and collaborative features make it easy for our team to work together, ensuring that insights are shared and leveraged across departments. With Deepnote, we're not just collecting data; we're transforming it into actionable insights that drive our product's success and enhance user satisfaction.

What do you like best about Deepnote?

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.

What do you dislike about Deepnote?

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.

What problems is Deepnote solving and how is that benefiting you?

Data analysis. Data science and machine learning around large data sets. Reporting interface for data in the database before building it out in the application layer.