Jupyter notebooks are one of the world's most popular interactive computing tools (approximately 10 million Jupyter notebooks on GitHub and counting), but that hasn't stopped businesses from seeking out Jupyter notebook alternatives.
Why? Because despite Jupyter's omnipresence in the data science community, it simply isn't always the right tool for the job --- especially for data teams.
We're all familiar with code smells, those pesky warning signs that all is not right with your source code. Let's look at three common circumstances --- what we call data smells --- that indicate it's time to seek out a replacement for your Jupyter notebook.
1. Your team wastes valuable time trying to reproduce analyses
The results of your analysis don't count if they can't be replicated, but that's not easy with a Jupyter notebook.
Essential elements of your analysis, such as the environment and required files, are typically bound to your local computer. For your team to reliably reproduce it, they'll need everything --- not just the notebook file itself.
Team members will have to tediously configure their environments to match yours and make sure they have exactly the same assets if they want to reproduce your analysis.
Ditto for getting access to the same data sources. That process usually goes something like this: Your teammate hunts down the proper credentials, searches for how to connect their notebook to a specific data source online, reads through the documentation, installs the necessary Python packages, works through the command line, and so on.
And when it's time for a colleague to duplicate the same setup? Back to step one. Those tight feedback loops you're after remain out of reach with out-of-the-box Jupyter.
But that's not the case with today's cloud-based notebooks. These Jupyter notebook alternatives allow data teams to share the same execution environment, file system, and data connection simultaneously for real-time collaboration.
The ability to run reproducible environments in the cloud is enough to turn skeptics into advocates.
"I was personally skeptical about the performance of a collaborative, hosted platform, but when I used Deepnote to collaborate with a colleague and we were able to remotely troubleshoot and try different plots, I immediately saw its value," said Webflow's Senior Manager of Data Science & Analytics Allie Russell.
Team members can connect to any data source, work in their browser, and share connections and environments with teammates who have been granted the proper user permissions. It's as easy as adding your credentials or API keys, which are then encrypted and securely stored. What was once a frustratingly convoluted process now only takes a few clicks.
"We'd had a lot of technical issues when trying to pair up on other notebooks during remote interviews," said Gusto's Product Analytics Lead Becca Carter. "Deepnote was incredibly easy to set up and allows us to start new notebooks in seconds."
2. Your team resorts to screenshots and PDFs to communicate findings
Data teams are tasked with uncovering business insights, validating them with their peers, and making them actionable for business stakeholders.
In other words: Data collaboration is critical.
But sharing insights isn't so straightforward with Jupyter notebooks. You're usually forced to grab screenshots and port them to a document or download notebook files as static PDFs. These quickly become outdated, and the whole process must be repeated any time there's a follow-up question or data refresh.
But modern data notebooks can be shared via email invitation or simply by sharing a link --- no different from Google Docs.
This helps support faster, more collaborative iteration.
"Working in Deepnote is like code review and rapid prototyping at the same time, saving valuable time in the iteration cycles," said VantAI CTO Luca Naef. "But as opposed to code review via GitHub, you have direct access to the runtime and program state, which makes understanding complex models much easier and leads to much more spontaneous creative ideas."
And when it's time to share your analysis with non-technical teammates, you can publish notebooks as code-free articles or interactive applications. You and your teammates can tag one another and leave each other comments to collaborate in real time or asynchronously.
It's this ability to better facilitate collaboration with business stakeholders that drives many companies to look beyond Jupyter notebooks.
"Since metrics require a lot of input from subject matter experts, data consumers, and business stakeholders to define and align on definitions, we needed a collaborative layer where we could get immediate feedback," said Slido's Head of Analytics Engineering Michal Koláček.
3. Your team struggles to work together on interrelated projects
Data notebooks are synonymous with exploratory programming, but quick experiments and prototypes shouldn't be seen once and then forgotten. Projects often grow over time, changing hands and evolving alongside the needs of the business. This makes discoverability key.
But Jupyter notebooks create silos. There's no single place where your analysis is stored, organized, version-controlled, and made easily accessible to the company.
This is another area where modern notebooks excel. Teams can create workspaces where data science and analytics teams can share analysis (and narratives) with both technical team members and business stakeholders. Notebooks, articles, and applications can all be organized into a customizable folder structure that's tailored to how different teams and companies operate.
These workspaces act as searchable databases that scale as teams and their projects grow, complete with granular permissions that dictate who can access which projects and what actions they're allowed to take, from viewing to commenting to editing. A project's entire history can be tracked and reviewed, and older versions of notebooks can be previewed and instantly restored.
This allows data teams to ensure sensitive information stays secure while democratizing data access for both technical and non-technical teams. That's why one Deepnote customer --- a hedge fund with a large data science team --- sought out a Jupyter notebook alternative that would act as a living knowledge base, not a siloed tool.
"Notebooks are often used as a quick prototyping tool, but we don't want to create one-off work," the customer said. "We want to invest in ideas that compound over time. Deepnote gives our team one place to create, store ideas, and build on top of the work of others. Visibility goes up over time."
These data smells aren't deal-breakers for everyone. Plenty of Jupyter notebook users aren't operating in a traditional business environment. Perhaps your work is strictly solo, which makes collaboration challenges a moot point. Or maybe you see the technical limitations as a fair tradeoff for a free tool.
But every busted workflow and lost second counts for business users. If you work on a team where time is of the essence and collaboration is the norm, consider these smells your cue to find a Jupyter notebook alternative.
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