Hex.tech vs. Jupyter Notebook

Hex.tech is an excellent tool for a product intelligence or product analytics team that wants to enable widespread use of available data for decision-making. It requires some SQL experience in many cases but makes it possible to go far without complex querying skills.

Incentivized

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.

Incentivized

• Powerful interactive notebook environment
• Balances the need of code and non-code users
• Provides free and easy sharing to viewers of analyses across the company
• Responds promptly to support requests and needs
• High quality native charting capabilities

Incentivized

• 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.

Incentivized

• Limited support for R notebooks
• Limited sharing outside of the app (e.g., no slack integration)
• Some Python knowledge needed for highest levels of aggregations

Incentivized

• 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.

Incentivized

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.

Incentivized

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

Incentivized

Hex.tech is the easiest to use, most enjoyable experience, most powerful environment, and most cost scalable of all the above.

Incentivized

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.

Incentivized

• Increased visibility into analyses with free membership tier across company
• Increased capacity for self-service analytics among R&D stakeholders (e.g., product managers)
• Improved productivity of data science team with ease of use

Incentivized

• 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