CARTO vs. Jupyter Notebook

CartoDB is great for generating geographic visualizations of data where the geographies are well-defined. It would be great for analysts to develop visualizations of data with spatial elements. That being said, the software is limited if you want to do any real data munging or analysis, as it can be cumbersome to use and there isn't a great interface for actually saving the results of different manipulations (you can save it as a new file, but it's hard to do version control, etc.). I would recommend preparing the data outside of CartoDB and only using the tool for visualization once the data is well prepared.

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I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

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• It is amazing at allowing control of the visualizations. It takes a little bit to get used to but the combination of full SQL queries and CSS-like styling is very powerful.
• The services are built on a robust stack of open source software. I was able to build a standalone instance of CartoDB relatively easily (after some research and trial and error).
• Server side map rendering is key for handling large data sets. The way the images are returned makes them very easy to catch in an HTTP cache to minimize the hits to the server. The interactivity that CartoDB has built in makes this completely transparent to the end user, they can click on parts of the static images and be presented with popups or change map styles. It's a very clever implementation.

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• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

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• I don't like the new Builder UI as it has been tough to explain. Other than that I have no complaints.

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• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

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Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

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I haven't had a need to contact support. However, all required help is out there in public forums.

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CartoDB is more user friendly and quicker to learn than ArcGIS Online and Mapbox

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With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

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• CartoDB definitely saves a lot of time when creating visualizations. Previously, I would use different software and have to make edits manually (or just create the visualizations manually to start with). I would say that the software definitely cuts the time required to create certain visualizations by a half or two-thirds.

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• Positive impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

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