Colasoft Capsa vs. Jupyter Notebook

Capsa is highly recommended to be used in small dense areas of a network where it makes sense to be able to run analytics off of a mirror port to gain insight into traffic trends. It is a costly product to install in branch locations, and requires a decent spec hardware to run appropriately

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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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• Robust Application. Able to handle traffic that most businesses will throw at it.
• Easy to use. Easy to setup captures and filter down to specific traffic types.
• Relatively cheap for what it's able to accomplish.

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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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• Needs options to scale to 10Gb/s networks. The cutoff for performance looks to be around 3Gb/sl
• Needs API access to trigger capturesl
• Needs Agents for remote capturesl

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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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Colasoft performs more like the traditional wireshark as opposed to other solutions that tend to focus on graphs instead of giving you the raw data. I personally prefer the raw data, and then the ability to build graphs upon this (which Capsa can do). This allows for quicker analysis and makes it easier to relate to.

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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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• While the product doesn't detect malware, it has helped track down malware infected machines by using data from other applications to run filters to look for known traffic destinations for that malware. For me this is a huge ROI since malware can be the great unknown.
• Relatively cheap compared to the feature set.

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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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