![]() ![]() Need to share your work for a code review? Static GitHub repository it is. Want to code with a teammate simultaneously? Not possible. This creates a knock-on effect that slows down everyone.īut connectivity limitations aren’t the only problem. 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). It took longer than it should have, but the hard part is over, right? Say you’ve connected to the right data sources, configured your environment, and installed the necessary Python packages. This is where offline notebooks get downright sluggish. For insights to become actionable, they need to be reproduced by your teammates and shared with stakeholders. Locally hosted notebooks may be faster for running local datasets, but analysis doesn’t happen in a vacuum. And that means increasing team velocity, not just solo work. When it comes to exploratory programming, speeding up time to insight is the top priority. 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). 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? 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. Why would you want to take your Jupyter notebook online? Because a notebook interface that’s stuck on your laptop slows you down. ![]()
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