Deepnote
DeepnoteReviews from AWS customer
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Deepnote is a game changer
What do you like best about the product?
The cloud based infrastructure allows our data science team to get answers much more quickly and collaborate much more effectively. As a sponsor of the technology, I highly recommend it
What do you dislike about the product?
There is not much room for improvement. Speed enhancements are always welcome
What problems is the product solving and how is that benefiting you?
Deepnote allows us to analyse our data much more effectively
More than just a managed Jupyter notebook
What do you like best about the product?
It's a combination of lots of little things: pre-configurable integrations (e.g. database connections) that drop into the flow as executable blocks, excellent data visualisation tools that let you interact with the data with almost no cognitive overhead, and the ability to schedule runs / build interfaces for easy workflow automation.
What do you dislike about the product?
It can be a bit aggressive about spinning down your instances
What problems is the product solving and how is that benefiting you?
Deepnote replaces a local Jupyter environment as a swiss army knife to solve data wrangeling, automation and visualisation problems
Make easier your process with and from data
What do you like best about the product?
Powerfull tool to make analysis, create control tables, execute process and alerts. Easy to use
What do you dislike about the product?
The cost, so i have to control how many people has access to build with it
What problems is the product solving and how is that benefiting you?
complex data analysys
Alerts and some process automation
Alerts and some process automation
Exceptional collaborative data science platform that revolutionized our workflow
What do you like best about the product?
What I love most about Deepnote is its seamless real-time collaboration features combined with its intuitive interface. The ability to work simultaneously with team members on Jupyter notebooks while having built-in version control and commenting systems has completely transformed how we approach data science projects. The platform's integration with popular data sources and cloud services, along with its powerful compute resources, makes it incredibly easy to go from data exploration to production-ready insights without the usual infrastructure headaches.
What do you dislike about the product?
While Deepnote is fantastic overall, there are a few minor areas for improvement. The pricing can become steep for larger teams or when requiring extensive compute resources for extended periods. Occasionally, I've experienced slight lag during peak usage times, and some advanced customization options that power users might expect from local Jupyter environments are still limited.
What problems is the product solving and how is that benefiting you?
The cumulative effect is that I can now focus on the actual data science work rather than fighting with tools and infrastructure. My productivity has increased significantly, and collaboration with my team has become seamless and enjoyable rather than a source of friction.
Nice idea, but to many bugs and missing features
What do you like best about the product?
Concept and Idea: The overall concept of ReView is appealing and innovative. It attempts to address key pain points in notebook and task management.
User Interface: Initially, the UI appears intuitive, visually appealing, and user-friendly, creating a positive first impression.
User Interface: Initially, the UI appears intuitive, visually appealing, and user-friendly, creating a positive first impression.
What do you dislike about the product?
Lack of Professional-Grade Features: Over prolonged usage, it's evident that ReView isn't fully tailored for professional environments, particularly IT professionals and engineering teams.
GPU Utilization Visibility: There's no capability to monitor GPU usage for notebooks running on specialized GPU instances, which is critical for machine learning tasks.
Versioning Issues: Proper version control mechanisms are insufficient, posing difficulties in maintaining and managing notebook iterations effectively.
Authentication Problems: OpenID Connect authentication for AWS integration doesn't function, significantly hindering cloud-based workflows.
Instability of Task Execution Order: Tasks don't consistently execute in the intended order, forcing repetitive executions of notebooks. This is particularly detrimental for iterative machine learning processes.
GPU Utilization Visibility: There's no capability to monitor GPU usage for notebooks running on specialized GPU instances, which is critical for machine learning tasks.
Versioning Issues: Proper version control mechanisms are insufficient, posing difficulties in maintaining and managing notebook iterations effectively.
Authentication Problems: OpenID Connect authentication for AWS integration doesn't function, significantly hindering cloud-based workflows.
Instability of Task Execution Order: Tasks don't consistently execute in the intended order, forcing repetitive executions of notebooks. This is particularly detrimental for iterative machine learning processes.
What problems is the product solving and how is that benefiting you?
Training ML Models
Deepnote helped us turn raw data into real-time insights and smarter operations
What do you like best about the product?
Deepnote makes pulling and exploring data so much easier. We can quickly connect to databases and get straight into analysis, which has really improved our visibility into performance and helped speed up decision-making. It’s also incredibly flexible — we’ve been able to build models that help ensure operational quality and adapt them as our needs evolve. Plus, the real-time collaboration, SQL/Python support, and zero setup make it perfect for remote teams.
What do you dislike about the product?
The main issue is the loading time when working with larger datasets or multiple data sources — it can be a bit slow to initialize. Also, since it’s fully cloud-based, it doesn’t work well in low-connectivity situations, and offline access would be a helpful addition.
What problems is the product solving and how is that benefiting you?
We use Deepnote to centralize and streamline our analytics workflow. It’s helped us move faster from data to insights, improve transparency across teams, and build custom models that support quality control in operations. Overall, it’s made our data work more efficient and collaborative.
Efficient analysis tool
What do you like best about the product?
The ability to export analyses from the app mode: once they are edited, you just have to press a button to launch them and retrieve the information you want. When you have no knowledge of coding, it's ideal.
What do you dislike about the product?
To my knowledge, no drawbacks, I use it to extract data, our data analyst has programmed everything in advance.
What problems is the product solving and how is that benefiting you?
Quick analyses of our sales, our customer databases, and purchase predictions.
Great Review for DeepNote
What do you like best about the product?
I've been using Deepnote for my Python projects, and it's been an absolute game-changer. The interface is clean, intuitive, and incredibly well-suited for both solo work and team collaboration. I especially love the real-time collaboration features—feels like Google Docs for data science. It also integrates smoothly with popular tools like GitHub and BigQuery, which saves me a lot of time. The fact that I can run Jupyter-style notebooks in the cloud without setting up a local environment is a huge plus. Highly recommend it for anyone working on data science, analytics, or machine learning workflows.
What do you dislike about the product?
sometimes a little slow but its a good feature to work with.
What problems is the product solving and how is that benefiting you?
Deepnote is solving the problem of fragmented, non-collaborative data science workflows. Traditional Jupyter notebooks lack real-time collaboration, version control, and seamless integrations, which often slows down project development—especially when working in teams. Deepnote fixes that by offering a cloud-based, collaborative environment where I can work with others in real time, similar to how Google Docs works for documents.
This has massively benefited me by increasing productivity, reducing setup time, and allowing smoother handoffs and code reviews. I no longer have to worry about syncing notebooks manually or running into environment conflicts. It also helps me stay focused on the actual problem-solving and analysis rather than on infrastructure issues.
This has massively benefited me by increasing productivity, reducing setup time, and allowing smoother handoffs and code reviews. I no longer have to worry about syncing notebooks manually or running into environment conflicts. It also helps me stay focused on the actual problem-solving and analysis rather than on infrastructure issues.
Big fan of the usability and features. Small number of bugs that I would like to see fixed
What do you like best about the product?
I like Deepnote because it's a lot simpler to use than other notebooks I've tried. It integrates really easily with tools like Redshift and S3, which makes connecting to data super straightforward.
The AI assistant is surprisingly good at fixing errors in my code, which saves me time when something breaks.
I also find it really useful that I can search our data warehouse directly for fields or tables - it makes digging into data way faster.
One of my favourite features is being able to search for code snippets. This is especially handy when I’m not sure which script is publishing a data source I’m using in Tableau. Instead of guessing and opening a bunch of files, I can just search and find the right one quickly.
Customer support tends to be quick to repsond based one my one interaction with them.
The AI assistant is surprisingly good at fixing errors in my code, which saves me time when something breaks.
I also find it really useful that I can search our data warehouse directly for fields or tables - it makes digging into data way faster.
One of my favourite features is being able to search for code snippets. This is especially handy when I’m not sure which script is publishing a data source I’m using in Tableau. Instead of guessing and opening a bunch of files, I can just search and find the right one quickly.
Customer support tends to be quick to repsond based one my one interaction with them.
What do you dislike about the product?
Sometimes scripts fail when I’m copying data to Redshift - could be a Redshift issue, but it’s still frustrating when it happens.
I’ve also noticed that if I’m working on a script for a few hours, Deepnote can get really slow. Restarting doesn’t seem to fix it, so I usually just have to wait until the next day to carry on.
Another small issue is how the script jumps around when I first open it and try to scroll quickly - it makes it hard to get where I want to go without it lagging or skipping.
I’ve also noticed that if I’m working on a script for a few hours, Deepnote can get really slow. Restarting doesn’t seem to fix it, so I usually just have to wait until the next day to carry on.
Another small issue is how the script jumps around when I first open it and try to scroll quickly - it makes it hard to get where I want to go without it lagging or skipping.
What problems is the product solving and how is that benefiting you?
It allows me to create scripts which extract, clean and transform my data from our data warehouse in redshift to turn it into a useable data source to be used in Tableau for our end users.
I also find it useful for searching our data sources quickly, or perform quick EDA to better understand the data. This would not be possible without using a notebook, and Deepnote makes the process much easier.
AI Chat has been useful to document my scripts.
I also find it useful for searching our data sources quickly, or perform quick EDA to better understand the data. This would not be possible without using a notebook, and Deepnote makes the process much easier.
AI Chat has been useful to document my scripts.
Quick way to incentivate a no-tech team to start to code
What do you like best about the product?
The building blocks approach are very helpful, easy way to automatize small tasks to connect databases, transform and analyze data
What do you dislike about the product?
The IA tool is not very helpful, i prefer to use chat gpt or v0 to help me
What problems is the product solving and how is that benefiting you?
ETL for marketing: offline conversion tracking uploaded back to ads platforms. Lead scoring
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