Jupyter Notebook vs. Mode Analytics

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

Mode is a clear category winner for how it easy it is to go from interactive analysis to professional quality reporting. You can easily do all your SQL and quite a bit of R/Python. If you want to quickly go from your database to simple charts, this is your pick. If you need to do heavier data modeling, scheduling jobs, or ML, you'll need something else, like Databricks. In my opinion, the quality of visualizations and some developer/analyst features make Databricks an unacceptable substitute.

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

• It allows me to undertake data visualization tasks by creating really insightful charts and dashboards
• It allows for easy sharing and work collaboration on data analysis tasks
• Mode has an easier to use SQL editor as well
• I love how it organizes all our SQL data tables into easy-to-view data we can draw insights on

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

• Date ranges aren't always the most intuitive to set, strangely
• Faster refresh rates would be ideal
• Would love more dynamic mobile device integration (alerts, etc)

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

For the advanced user already familiar with Python. It is just the same tool, already managed and connected to the data.

Incentivized

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

Incentivized

Mode has dedicated agents who are online in a chatroom, ready to take on any questions and provide helpful feedback.

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

We used both Qlik and Mode at our organization. The mode was used a quick analysis SQL tool while Qlik was our enterprise BI solution.

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

• Extremely positive. It's a key feature in our application.
• Were it not for the account management bit, Mode would be hands-down our recommendation for anyone looking for cloud BI.

Incentivized