JarviX vs. Jupyter Notebook

JarviX is very good at data integration, creating charts and reports for data visualization. It is really impressive that Jarvix can let our marketing team know the number of members living in a specific area in seconds. In addition, the product manager from Synergies is willing to help us with all problems we encounter. I accidentally changed one of the settings in a data table which threw the existing dashboard into chaos. However, after assistance from the product manager, we were able to successfully bring the data back to normal.

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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• The algorithm platform is handy
• Time-saving
• Real-time data update
• Database docking

• 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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• There should be more flexibility in changing the data tables uploaded into JarviX. For example, it should enable users to edit the syntax of the table or change the data format of the columns without affecting the existing dashboard and charts already created using that data table.

• 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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JarviX is very easy to use, once everyone learns how to use JarviX to produce their own data reports, it reduces IT’s workload.

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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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Independently Invited by TrustRadius

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

JarviX is very easy to use, once everyone learns how to use JarviX to produce their own data reports, it reduces IT’s workload.

Incentivized

Invited by the Vendor

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

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

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Tableau and Power BI. Obviously, they are not in the same category. Tableau and Power BI only provide data visualizations plus a little bit of analytics. JarviX not only provides data exploration, so you can find out what the problem is, as well as model management and App builder. If you only look for a dashboard, yes BI is probably enough, but if you constantly feel lacking actionable insights for actual implementations of improvements, JarviX is the choice for you.

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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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• I think the best part of JarviX is that we can get the analysis results in several minutes. We spent lots of time making reports and managing our data in the past. All in all, it helps us save the time, cost, and labor of managing data.

• 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

Incentivized

Independently Invited by TrustRadius

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