Overall Satisfaction with KNIME Analytics Platform
My team uses KNIME Analytics Platform to build a variety of Data Science Pipelines. These KNIME workflows are then published through KNIME Server that can help hosting a front end for our end users across many different organizations. The KNIME workflows that we built have many different capabilities, ranging from data extraction, pre-processing, model training and optimization. We also build some self-services analytics platform using KNIME as well as automation tools.
- Easy to use without much knowledge of coding.
- Connection to other languages such as JS, R, Python, etc.
- Workflow is displayed as connected nodes which makes it easy to troubleshoot and visualize.
- Have a decent size community that supports Q&A.
- Execution on other programming languages is slow.
- Workflows are very big even building a very simple one due to caching and GUI.
- Can frequently stop working and quit unexpectedly.
- Provide a quick and dirty way to build models.
- Allows fast deployment if you don't care about scalability.
- Slow in execution.
- KNIME Server license is on strange format.
- Good for R&D and small company.
KNIME Analytics Platform has a nice visualization comparing to Azure Machine Learning Studio. KNIME also has a good amount of built-in preprocessing nodes and ML training nodes that makes it easier to develop workflow instead of writing codes. However this also limits the flexibility and freedom in customizing your workflow and one has to constantly think about workarounds.
KNIME's HQ is in Europe, which makes it hard for US companies to get customer service in time and on time. Their customer service also takes on average 1 to 2 weeks to follow up with your request. KNIME's documentation is also helpful but it does not provide you all the answers you need some of the time.
Do you think KNIME Analytics Platform delivers good value for the price?
Are you happy with KNIME Analytics Platform's feature set?
Did KNIME Analytics Platform live up to sales and marketing promises?
Did implementation of KNIME Analytics Platform go as expected?
Would you buy KNIME Analytics Platform again?
If you have a team of engineers or data scientists who do not like to code, KNIME can be a good platform to build quick and dirty pipelines. However if you are moving away from R&D to deployment, KNIME lacks the scalability compared to Python or R itself. When deploying, you can choose to output json or use their native front end from KNIME Server, but KNIME Server is not free.