Jupyter Notebook vs. Rational BI

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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Definitely well suited for small companies, but again many of their competitors are also well suited in this segment. We were in general happy with the solution provided, but I'm not blown away by their solution or support. If you want to get more data-driven visual boards with data displayed in nice graphics is in my opinion a good start, and here Rational BI delivers what it should.

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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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• Arrange data.
• Dashboard.
• Automatic reports.

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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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• Not seeing anything at this time, comprehensive product.

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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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Overall Rational BI is a valuable enterprise reporting tool for any data driven organization. It offers great depth and breadth of features for reporting and analytics that can lead to better business outcomes. Its easy to use and highly configurable to evolve to changing reporting needs of organizations of any scale.

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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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I haven't used the support myself, but my colleagues have been satisfied with the support. As I have understood from my colleagues the support is as you could expect. Still, the documentation could be better and that could avoid the need to contact their support, but overall we're still happy with the support as well.

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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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Rational BI allows a deeper data analysis with respect to the other software I experimented with. The velocity to perform the analysis is similar to the other one. The predictive analysis could be very useful, but at the moment I do not use it in my activities. Dashboards are nice and easy to understand.

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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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• Allows folks to work on other items as reporting is done in the background.
• Scheduling and delivery of reports with one click.
• Single pane of management.
• Ease of use.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info