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. Those who have access to your shared notebook can save a copy as their own.Why would you want to take your Jupyter notebook online? Because a notebook interface that’s stuck on your laptop slows you down. Other items in your organization, allowing them to open and run the Share your notebooks with other users in the same way you do any Notebooks to see how you can use notebooks to manage your Sending notifications to members of your organization, managingĬredits and licenses, and monitoring the security of your Manage your organization and contentĪs an administrator, you can use notebooks to automate tasks such as Use raster analysis tools to extract information from imagery and raster data-for example, by calculating slope, estimating vegetation coverage, and performing change detection.Ībout performing analysis with notebooks 4. Use ArcGIS API for Python or any of the featureĪnalysis tools to perform analytical functions such asįinding hot spots, routing, and accessing a geodatabase. Hosted notebooks to answer questions, drive insights, and create visualizations of your data. You can also upload content to the workspaceĪnd download content from the workspace to your machine. UseĬontent from your organization, ArcGIS Online, or ArcGIS Living Atlas of the World. Browse your feature layers, imagery layers, and web tools to add them to your notebook. To perform data science with notebooks, you need hosted content. The runtime that includes the libraries you need. Three runtimes areĪvailable with different resources: Standard, Advanced, andĪdvanced with GPU. Made available through a notebook runtime. The resources and Python libraries you use in your notebooks are
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