Metrics on productivity gain


Could you give me some metrics I could refer to about how much faster it is to create a (complicated) workflow with KNIME than it is in Python?

Thanks a lot,

I am asking if we have any metrics like this internally.

What do you use Python for? Data Science? Data Engineering? Data Visualization?

Here is a simple example which I often find to be time consuming in Python, but takes very little time in KNIME:

Screen Shot 2022-04-11 at 8.32.44 AM

You can find many examples when you download the free KNIME Analytics Platform:

If you deal with ML models, then check out how little time it takes to make a DBSCAN model:

Or webpage retrieval:

Or data viz (just connect your data):

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Hello again,

Thank you for your reply.
I am looking for some reported metric for complex data science projects.

With (waning) enthusiasm, I try to explain the benefits of using KNIME to hiring managers, potential clients, etc., and I’d need metrics for this. I seem to be unable to hit a hole on the Python wall. Everybody wants Python for data science.
Unfortunately, even though I could work in Python, I really don’t want to. It’s so not fun compared to KNIME, so slow, so prone to errors, so annoyingly inefficient… So I need metrics to support my point, if you have any.


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What metric in particular would you like to see? Could you give 2-3 concrete examples? We may not have them on hand, but this could be a really interesting mini project for the KNIME Team to think about.

I didn’t go to complicated:

  • How fast is it to learn KNIME for an accomplished DS?
  • How much faster is it to create solutions in KNIME compared to Python. I saw reports like this on LinkedIn, but haven’t saved them. :frowning:

I see. For me, I was using and preferring KNIME over programming within 1 month of usage.

For the second question, I will ask around to some of our Pythonistas. Thanks for the idea!

Talking about clients: I think the best metrics you can use is the well know commercial metrics. Implementing use cases with KNIME should pay back with a lower price for the client.
This should include aside the implemention costs also costs for software and hardware.


KNIME seems like the ideal python transition platform to me. Not only is the desktop version of KNIME open source, but python code can also be incorporated into KNIME workflows. Maybe you could create some project applicable workflows with alternate process branches showing python code only vs node only solutions to help illustrate the potential and accuracy. Another big sell is that KNIME workflows are more easily reviewed and internalized by outside parties who lack a programming background, and greater KNIME literacy and adoption across an organization will open countless new doors for process automation and improved decision making.