Why would you want to take your Jupyter notebook online? Because a notebook interface that’s stuck on your laptop slows you down.
When it comes to the bread and butter of modern data teams — collaborating on analysis and sharing the results — offline notebooks leave you holding the bag. Data notebooks allow you to query, code, build data visualizations, and craft narratives with text all in one place, but what good is that work if it’s chained to your local machine?
Let’s look at how an online Jupyter notebook is designed to meet the needs of today’s data teams (and dispel a few myths along the way).
Every second counts
When it comes to exploratory programming, speeding up time to insight is the top priority. And that means increasing team velocity, not just solo work.
Locally hosted notebooks may be faster for running local datasets, but analysis doesn’t happen in a vacuum. For insights to become actionable, they need to be reproduced by your teammates and shared with stakeholders. This is where offline notebooks get downright sluggish.
Say you’ve connected to the right data sources, configured your environment, and installed the necessary Python packages. It took longer than it should have, but the hard part is over, right?
Tell that to your teammates. Setup from scratch is required each time a new team member wants to execute your notebook (cue the inevitable errors and time spent investigating what’s causing them). This creates a knock-on effect that slows down everyone.
But connectivity limitations aren’t the only problem.
Want to code with a teammate simultaneously? Not possible. Need to share your work for a code review? Static GitHub repository it is. Ready to pass along the results of your work to a non-technical colleague? Time to start copying and pasting screenshots into a document (and repeating the process for every follow-up request).
Data is a team sport, but locally hosted notebooks create silos. There’s no reliable way to collaborate on offline notebooks — either in real time or via comments. Teamwork becomes a tedious back-and-forth that eats up valuable time.
Old habits die hard
So why haven’t all data teams switched to online Jupyter notebooks?
Habit and familiarity play their parts. Jupyter notebooks are among the most popular interactive computing tools in the world, and that’s made offline notebooks the de facto standard until now (sure, configuring your own JupyterHub instance could help, but analysts want to analyze data, not manage all that infrastructure).
There may also be misconceptions about collaborative, cloud-based data notebooks.
For instance, some companies are wary of having their data live anywhere than on their own physical servers. They’re rightfully concerned about keeping data safe and secure. But locally hosted notebooks open up the door to poor security practices.
How do you securely store your secrets? And, just as importantly, how do you permit them to be used by your team without exposing them? To avoid slowdowns, team members often end up sharing their credentials and sending screenshots containing sensitive data.
There’s also the myth of the lone data professional to contend with. Companies may overlook the need for teammates to collaborate in the same compute environment at the same time when multiple stakeholders are involved in analysis and its outcomes.
If you’re a lone ranger who doesn’t need to collaborate with teammates or business stakeholders, working locally in Jupyter is fine. But that’s rarely the case for modern businesses.
Cloud-based notebooks are built for collaboration
Cloud-based data notebooks make teamwork seamless instead of a slog.
Teammates can code from the comfort of their browsers in a fully managed and completely customizable cloud-based environment. Even better, that same environment — including data connections, secrets, libraries, etc. — can be accessed with a link or email invite. No more time spent on setup.
Granular permissions dictate who can view, comment on, and edit notebooks, helping you keep data secure. And built-in version history makes previewing and restoring older versions of notebooks (as well as tracking any and all changes) as easy as clicking a button.
As long as your teammates have the right permissions, they can jump into your notebook at the same time and code right alongside you (or simply tag you and others in comments).
Notebooks can instantly be published as articles, dashboards, and interactive data apps for non-technical colleagues. Need to make a tweak (or schedule a recurring run to keep data fresh)? Do it on the backend and watch your updates magically materialize.
And you can do it all without the hassle of machine management and upkeep. Computing power and storage capacity scale automatically based on your needs.
Locally hosted data notebooks aren’t built for the business of collaboration. Cloud-based notebooks are.
If you’re looking for a fast, secure, and scalable way to work better together, it’s time to move to an online Jupyter notebook.
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