KNIME Reviews

It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript. 

It's very general in terms of architecture, and as a result, it doesn't support efficient running. That is, the speed needs to be improved.

The scalability needs to be improved as well.

It's more or less stable.

It is scalable to a certain extent.

It is fairly easy to setup KNIME.

The client versions are mostly free, and we pay only for the KNIME server version.

It's not a cheap solution.

For experimental work, KNIME is a good solution. However, for special task oriented developments, it's not the best.

Considering this, I would rate KNIME at seven on a scale from one to ten.

I mainly used it to perform predictive modeling projects, such as customer-churn predictions and HR attrition predictions. The environments are mainly SQL-databases or CSV files.

The installation I worked with to perform the analyses was a regular laptop with no computational server behind it, which may have an impact on the capacity of the program handling very large databases or files.

The clients I performed the analyses for were all very pleased with the results. For churn prediction, one of the companies proactively started contacting clients with high risk to churn, resulting in drastically decreasing churn rates.

For organizations with a small team of data analysts or data scientists, it is a very easy tool to become familiar with predictive modeling, and makes it possible to hand over projects to colleagues without the need to extensively document them.

• The very easy-to-use visual interface
• Help functions and clear explanations of the functionalities and the used algorithms
• Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis

For inexperienced analysts or data scientists, it is a very easy tool to take your first steps in modeling and analytics.

The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R).

The program is not fit for handling very large files or databases (greater than  1GB); it gets too slow and has a tendency to crash easily.

I used it quite intensively for 10 months, long enough get familiar with it, to follow training, to use it in in several projects, to ask questions on the user forum.

All analytics individuals use KNIME to integrate multiple sources of data (SQL, excel, etc.) and prep the data for static reporting.

We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics.

• Visual workflow creation
• Workflow variables (parameterisation)
• Automatic caching of all intermediate data sets in the workflow
• Scheduling with the server

The overall user experience feels unpolished. 

• Data field type conversion is a real hassle, and date fields are a hassle.
• Documentation is pretty poor.
• User community is average at best.

It is pretty stable.

Partially, only with very large datasets (10M+ records or so); its reliance on RAM is a bit high for normal PCs. Servers should be fine.

Not applicable (not local in South Africa).

Alteryx. KNIME is much cheaper. The KNIME desktop client is free. KNIME handles 95% of our requirements.

Straightforward.

KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required.

Alteryx.

I rate it a seven out of 10. It's very useful but needs polish and improved UX and UI in several areas.

For quick adoption, either get KNIME to provide training, or have a local knowledge expert on hand who is well versed with data workflow tools, and databases if necessary.

I use KNIME for clustering data analysis. 

It's a coding-less opportunity to use AI. This is the major value for me.

I had some difficulty connecting to servers. It asked me to set something up on my server and it asked me for a code that I needed to generate on the server. There were several steps that I messed up. I followed all of the instructions but I couldn't manage it at all. I followed the directions in several forums to find out the problem.  

There should be better documentation and the steps should be easier. 

I have been using KNIME for three to four months. 

I haven't needed to contact their support. 

I tried Python and Microsoft. 

The initial setup was super easy. It was really quick. I did it myself for personal use. It didn't take longer than half an hour. 

Some of the samples are outdated but my advice to someone considering KNIME is to use their samples. 

I would rate KNIME an eight out of ten. 

In the next release, the should have more comprehensive samples.

Our analysts use Knime in the company for data modeling, data wrangling, and data preparation. We have a good amount of data that we work with.

I do not personally use the product, but I am familiar with its usage through my analysts.

Data preparation and data modeling are easy to do.

It is very fast to develop solutions.

There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool. This would make workflow development faster because several tools could be used together, based on the function that is chosen. Each would complete one of the constituents of the task.

We have been working with Knime for approximately one year.

This product is quite stable and we haven't had any problems.

Knime is a scalable solution and we haven't experienced any issues. There are six of us who are using it.

Prior to Knime, we were using Alteryx. However, Alteryx is too costly and our customers don't want to pay for it.

The initial setup is easy.

The price for Knime is okay.

I would rate this solution a nine out of ten.

I am advocating the use of this solution in my organization. I use it personally for my purposes and for the company, I use it for internal data science with very good results.

What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.

It is difficult to say at this time, as I am not using the latest version. I have noticed that I don't have the latest modules that were added, such as ML.

In my opinion, there's one thing lacking. As far as algorithm notes go, it would be handy if category algorithms of C4 or C4.5 could be set with a checkbox or something like that.

Once you go to the forum or the documentation, to see how to implement a C4.5, you mark the checkbox, and only then it would be content for C4 or C4.5.

The documentation is lacking and it could be better. It's a community-driven product but there are a few crucial models missing such as ANOVA and MANOVA.

I have been using KNIME since December 2019.

In my opinion, it is very stable.

I have not yet explored the scalability as I am using it on my local machine and I don't have the experience of putting it on the cloud.

I do plan to increase my usage.

The documentation is okay, although there are things missing. At the same time, the forum support is great.

Previously, I was using SPSS Statistics, although that was ten years ago. I had a gap in data mining and the statistic field as a whole.

The initial setup is very straightforward.

It's an open-source solution.

I am considering further courses and maybe some certification in the next year.

I would strongly recommend KNIME. It's a modeling or statistics product that can be used by almost anyone with knowledge in the field. It works almost out of the box.

For starters, it's approximately two hours of watching videos and/or reading the documentation, and then off you go.

I built my first working model in two days when I started using KNIME, and it only needed to be tweaked. It was impressive.

I would rate this solution an eight out of ten.

We have used KNIME for text processing, specifically for leveraging the text processing features for entity extraction, document classification, relationship extraction, and other such NLP tasks.

We are far from reaping the benefits of this platform as an organization. However, so far, we have been able to appreciate the considerable reduction in prototyping time.

The most useful features are the readily available extensions that speed up the work. For instance, KNIME offers multiple document taggers, which one can use with relative ease. Similarly, the number of predefined NER taggers are also very handy.

The documentation needs a proper rework. 

Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing.

Lot of manual work has been automated through this and single solution created on this can be used by others, too. We have implemented the same thing in our organization as CAE.

Data handling capacity. Currently it handless 25M rows of data, after which you will face lagging issue.

System resource usage. Knime will occupy total system RAM size and other applications will hang.

I have been using it for 18 months.

Nope.

When the data limit exceeds, it will get lag.

A nine out of 10.

Previously, we used Alteryx for harmonizing. Cost was the main reason for the switch.

It is easy to setup.

It is free of cost. It is GNU licensed.

No.

It is best one for harmonizing data with no cost included.