![]() ![]() However I do not share the other reasons. I support the idea to integrate jupyterlabs into DSS, specially since apparently it will replace the Jupyter notebooks. One potential aspect to account for is the inherent filesystem. With standard Jupyter notebooks, our data science leads need to re-run time intensive/costly cells to catch up to a point where an analyst or another colleague needs help.Ī user could push various notebooks in a lab session to the flow sequentially. My team is global and all virtual now because of COVID. One key feature that's highly enjoyable for collaboration is kernel sharing. Side by side views and window organization help coders to be more effective on the Dataiku platform. Thus we spend a significant amount of time "productionizing" code in Dataiku before projects can be merged to main and deployed to auto.ĭefault features like expanding and collapsing, dragging and dropping cells help organize and enhance collaboration. My team spends a ton of time surfing through old code and test code in notebooks. Consoles would allow for more effective testing and keep books clean. It would enhance the quality of code in python recipes on the flow due to better documentation features and markdown + console support in a single view. While JupyterLab is not perfect, it helps to organize code and supporting files and folders more effectively than a standalone notebook. Notebook interfaces are notorious for producing dirty code. Lab offers a much more IDE like experience with better collaboration for coders, including debuggers and other extensions that help coders tremendously. JupyterLab will eventually replace Jupyter notebook.
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