Jupyter Notebook vs. Splunk Cloud

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

Splunk is excellent when all your data is in one location. Its ability to correlate all that data is intuitive (once the hurdle of learning the query language is overcome). It is also easy to standardize the presentation of information to the company. When data is siloed/standalone, other systems can be cheaper and faster to implement.

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

• This SIEM consolidates multiple data points and offers several features and benefits, creating custom dashboards and managing alert workflows.
• Splunk Cloud provides a simple way to have a central monitoring and security solution. Though it does not have a huge learning curve, you should spend some time learning the basics.
• Splunk Cloud enables me to create and schedule statistical reports on network use for Management.

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

• The SPL programming language that the queries are built in is not very intuitive.
• There should be a better repository of pre-built queries for what I would think of as common Active Directory usage monitoring.
• I would like to see more free training/familiarization information made available.

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

Overall, it is very usable. I would like if recent searches were saved for longer because I always have to refer to my notes when I'm looking for something specific and it's been a few weeks. But that's a small issue, and the actual search and browsing interface is easy to use and powerful.

Incentivized

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

Incentivized

Splunk Cloud support is sorely lacking unfortunately. The portal where you submit tickets is not very good and is lacking polish. Tickets are left for days without any updates and when chased it is only sometimes you get a reply back. I get the feeling the support team are very understaffed and have far too much going on. From what I know, Splunk is aware of this and seem to be trying to remedy it.

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

Splunk Cloud blows Sumo Logic out of the water. The experience is night and day. We went from several highly stressed IT security professionals who were unsure if the data they were getting was valuable, to very happy IT security professionals who can now be more proactive and get all the information they need.

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

• End-end visibility across your departmental silos
• Strengthen the overall global monitoring posture
• Move from Reactive to Proactive Monitoring
• Highly secure environment at your finger-tips
• Takes you away from managing infrastructure/administration, allows saving time & money. Reduce the overall TCO (Total Cost of Ownership)

Incentivized