KNIME Reviews

I use KNIME for my academic works.

KNIME is more intuitive and easier to use, which is the principal advantage.

For graphics, the interface is a little confusing. So, this is a point that could be improved.

I have been using KNIME for six months.

I'd rate the solution seven out of ten.

I am promoting the use of KNIME because of my background as a computer scientist and my experience programming in languages, such as Pascal, Python, and R. Many of my junior colleagues at the university lack proficiency in computing, and KNIME is an effective tool for introducing beginners to programming. The platform is user-friendly and does not require coding, making it accessible for those who can learn the basics in just an hour through video tutorials.

One way KNIME has improved our organization is by allowing us to perform analyses that we previously couldn't. We often start with data in Excel or CSV format, and the process of importing data from other software, such as SPSS or STATA can be challenging. With KNIME, the process is simplified, as we can easily import the data with a single node, making it quick and efficient.

There are many valuable features in KNIME. One of the most useful aspects is that it can read a wide variety of data file types. Additionally, the ability to manipulate data, such as deleting rows or columns, is very helpful. I also use many of the nodes for analyzing data, such as doing frequencies and cross tabs. I have used it for machine learning tasks, like decision trees and random forests. It also has neural network capabilities, but I am not an expert in that area, so I cannot comment on it.

It's a very powerful and simple tool to use.

KNIME has met all of my needs so far. It has excellent data visualization capabilities. Additionally, it has a text analysis package, which I haven't used. However, I am satisfied with the features currently available and it has a strong support community.

One area that could be improved is increasing awareness and adoption of KNIME among organizations. Despite its capabilities, it is not as well-known as other tools.  The advertising and marketing efforts to reach out to companies and universities have not been very successful.

I have been using KNIME for approximately two years.

KNIME is highly stable, it's been working for over 10 years.

In terms of scalability, I haven't personally pushed KNIME to its limits. I have used it to work with tens of thousands to hundreds of thousands of cases and it has performed well on my own Microsoft Windows 10 PC. It has completed everything I wanted to do within a maximum of 10 seconds, but usually much less, often taking only a second or two. It sometimes seems immediate, but I have not tested it with hundreds of thousands or millions of cases.

The server version is certainly scalable. However, I am not using that version. I am using the desktop version, known as the Workbench. The server version can handle large datasets, such as those found in genomics, proteomics, and chemistry databases that are in the millions, so it is clearly capable of scaling. I am not able to comment on the performance of the server version as I have not personally used it.

I have not contacted the company for technical support. They have a community hub where many users contribute and I have used that for assistance and it has worked well for me. I am not commenting on the company's specific support services, but rather on the facility provided by the company for users to communicate with each other. Often, you can't distinguish whether the person providing the advice is an official representative of the company or a fellow user.

The support provided by the community hub is excellent. You can post questions and usually receive a reply within 24 hours. Sometimes you even receive workflow that can be easily integrated into your own work, saving you the time and effort of retyping it.

The initial setup of KNIME is trivial. I only needed to download and it run.

For beginners, the free desktop version is very attractive, but the full server version can be more expensive. I have only used the free version and it offers a fair pricing system. I have been promoting it to others without any compensation or request from the company, simply because I am enthusiastic about it. I am not aware of the pricing for the server version, but it seems to be widely used.

My advice to others starting out with the solution is for them to look up videos on the solution because there are hundreds of them, but start with the small ones.

You can begin using KNIME with a one-hour introduction, which provides enough knowledge to complete most research tasks, but it does not cover all the fine details of the platform. KNIME offers tens of thousands of packages, or nodes, that are available for download to perform various tasks such as text processing or regression. It is not possible to learn all of it at once, it's best to start with analyzing data that interests you and then expanding your knowledge as you go along. The platform is reliable, as new features are thoroughly tested and it has never failed me in the many times that I have used it.

I rate KNIME a nine out of ten.

I manage our data analytics team for a client in the insurance industry and we use the solution for cancelation probation campaigns, ratio modeling, and automatic claim models. 

The solution allows for sharing model designs and model operations with other data analysts. Other solutions such as SAS, R, and Python consist of just the script which is difficult to share. 

The solution is a low-code platform which reduces data mining time and its platform includes a clickable icon for linking script. 

The solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon. 

I have been using the solution for three years. 

The solution is stable and performance is normal. 

The solution is scalable to an on-premises version but requires a licensing fee. 

Support and documentation for the solution are only available in English and I would like that expanded to other languages. I rate support a six out of ten. 

Neutral

I used SAS which is good for data wrangling because it displays most of the end results with one simple code. 

The setup is easy but inconvenient because it requires separate installs instead of loading as a package with control of the open source code like Python. 

I completed the setup myself by referencing available documentation and rate setup a ten out of ten. 

The laptop version has an initial software fee. Scaling to the on-premises version requires a licensing fee per user that is a bit expensive in comparison to R, Python, and SAS. 

The solution is easier to use than Python because it links script to icons without having to write it down. Python is more effective at minimizing our efforts during the native rendering process. 

I currently use the solution's desktop version but plan to introduce the on-premises version soon. 

I rate this solution a nine out of ten. 

We use KNIME for a lot of predictive modeling. We use it to grab data, prepare it for modeling, do automated machine learning analysis, sometimes forecasting, and then try to deploy the models into production.

Since KNIME is a no-code platform, it is easy to work with. You don't have to write any codes and try to fix all the bits and pieces of coding or the intricacies of the programming language. Instead, getting a quick data prep or big data and eventually running it through your hypothesis is pretty fast. It's not ideal for huge data sets worth gigabytes, but it's okay since very few people have big data sets.

KNIME is not good at visualization. I would like to see NLQ (Natural language query) and automated visualizations added to KNIME.

I have been using KNIME for two to three years.

Unless you are working with terabytes worth of data, KNIME is a stable solution.

The solution is scalable and can be used up to terabytes of data. Around two to three people are using the solution in our organization.

The solution’s initial setup is quick and easy.

One person can deploy the solution within ten minutes.

The solution is very essential when we require an explainable data modeling pipeline. We can show the workflows of KNIME to our customers and talk about it instead of showing the code and expecting them to read, which they can never do.

The process of providing KNIME to the client, how it works, where we get the data, what the initial data statistics were, and what we get in return are pretty explainable. We worked on multiple retail projects and insurance scoring projects.

KNIME is perfect for data pre-processing projects. The important thing is that when someone builds a KNIME workflow, we can quickly onboard and change it for something else. It means that we don't need to read and understand the code. It means that it's replicable and reusable.

If somebody does something, somebody else can quickly onboard and enhance, improve, or totally change the workflow from scratch. It's pretty hard and time-consuming for typical use cases where we utilize coding. KNIME's open-source nature has a good impact on our analytics work.

Recently, KNIME added something relevant to generative AI integration, which was a good move. Alteryx is slightly more powerful than KNIME, and Dataiku is more powerful than both KNIME and Alteryx. I sometimes work with the on-premises version of KNIME and sometimes the cloud version. The solution does not need any maintenance.

Users should quickly start using KNIME for whatever they want to do, and they'll learn it on the go easily. I would recommend the solution to other users.

Overall, I rate the solution an eight out of ten.

We use KNIME for analyzing data, for ETLs, and analyzing for machine learning.

KNIME is easy to learn. You can code with KNIME using the visual coding platform if you know how to code. If you're working in an account management or financial department, you can use KNIME to work with a huge amount of data quickly. You can use KNIME to schedule your workflows, send emails, and write codes.

The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data.

I have been using KNIME for eight years.

KNIME is a stable solution. In the previous version, sometimes KNIME would get stuck, and we had to restart the server too many times. Sometimes, we faced a lack of memory issues with the solution.

I rate KNIME an eight out of ten for stability.

Less than ten users are using KNIME in our organization.

I rate KNIME an eight out of ten for scalability.

KNIME’s technical support team responds quickly. You can write your problems in the solution's forum, and they will answer you.

KNIME's initial setup is not easy and needs someone who knows Linux to do it.

A Linux engineer can deploy KNIME quickly, whereas someone who doesn't know Linux will take longer.

There is no cost for using KNIME because it is an open-source solution, but you have to pay if you need a server.

KNIME is a perfect solution for small and big companies, especially people who are using Excel. KNIME is very easy to learn and implement, and doctors and lab personnel can use it. Lots of companies are supporting KNIME and writing their own extensions. Data analysts and data scientists are using the solution for ETI processes.

Overall, I rate KNIME an eight out of ten.

We have been using the most recent version. It's version 4.6.

10 August 2023 - It has now been upgraded to 5.0 and is, if anything, even more impressive, especially in its ability to use Python and its libraries.

Knime seems to keep getting better. Their open-source model seems to be working. The addition of AI both to help in the building of workflows and as a facility within a workflow once it is up and running seems to add a dimension. At the moment, though, the system is so rich and fully featured that I have explored only the surface of the new version (5.4).

To date, all my needs have been met by earlier versions of Knime. I am, though, confident that should I need to start using version 5.4, the process will be smooth, and the new functionality fit for purpose. Upgrades to Knime have always worked like that in the past and I would expect them to do so in the future.

I used to be a Pascal programmer, and then I did a bit of Python. It does many of the things that I would've had to do in code, but does so without using code. I don't think it does everything, but it does most of what I need to do.

It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional.

For example, I have one data set whereby all of the data is encoded and there was one variable called opinion or something like that and it had codes for what the topic was, which was being discussed, whether it was positive or negative, whether it was strongly worded or weakly worded, and so many other things like that.

I had to transfer those into columns, like sentiment, the strength of sentiments, topic being discussed. I had to split it up into columns, and I could do that very easily, like simple JavaScript, in their column expressions.

It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.

They are also very careful with things like lab variants and issued variants because they have some labs that develop nodes, and new chunks of code which are represented as an icon. They make it very clear that those lab ones are not fully tested, and they're very glad to get comments back if you have problems.

I haven't had that difficulty myself. They seem to be aware that they have the community there as their testing base, and they seem not to be embarrassed about that. They will tell you when they go wrong and try to put it right.

So far, I haven't had problems with it, so I haven't really thought about room for improvement. It's so much better than many other things. It's useful in that you can at least get people who are pretty averse to programming to start thinking about putting something into a program of any kind, because they can see what's happening.

It's visual. It's codeless. For some purposes, I'd want to add Python or R, but I haven't had to do that so far, so I haven't seen the shortcomings of it. There must be some. All software has shortcomings, but I haven't recognized any myself.

Not just for KNIME, but generally for software and analyzing data, I would welcome facilities for analyzing different sorts of scale data like Likert scales, Thurstone scales, magnitude ratio scales, and Guttman scales, which I don't use myself.

I use both Thurstone scales and magnitude ratio scales quite a bit, and they're very powerful. But I've always had to do all the analysis myself in some simple code. I don't think that's provided. You could probably include it in KNIME, but I haven't tried to do it.

If it just said, "Analyze scales," and you'd choose which sort of scale you want to analyze and it gave you the options of normalizing or reversing or whatever it happens to be, that would be helpful. There are lots of simple functions that you want to apply to scales, which would be useful in any software, including KNIME.

I have been using this solution for about a year, but most particularly in the last six months.

It's been remarkably stable, much more so than most software. They have an active community forum. Problems seem to get fixed pretty quickly. I haven't had problems, but other people do report problems. So, there must be problems there, I just haven't had any.

On the very rare occasions that I have to seek advice, I just post it to the forum and someone will offer advice.

Positive

Compared to RapidMiner, at the moment I would go for KNIME, but that's largely because I haven't used RapidMiner much for the last year. It may have improved enormously since then. It was a very good package. They do much the same thing.

I'm more familiar with KNIME, so I would be able to talk more about it, whereas for RapidMiner, I was very enthusiastic when I used it. KNIME is a bit cheaper in a sense.

In RapidMiner, you can have up to 10,000 rows of data free of charge. For many things that I do, 10,000 rows of data is enough. I use quite a few UK government surveys, and I get the raw data from the UK Data Archive. They're often of the order of 10,000, 8,000. So, under 10,000 rows. I could use it free of charge.

I just downloaded it and then ran it. The process really was that simple. If I need one of the extensions (e.g. text mining), the process is just as simple.

We implemented the solution in-house.

I have not formally calculated it, but it must be substantial.

With KNIME, you can use the desktop version free of charge as much as you like. I've yet to hit its limits. If I did, I'd have to go to the server version, and for that you have to pay. Fortunately, I don't have to at the moment.

I would rate this solution an eight out of ten. 

I'm unwilling to give anything a ten because everything can be improved. But it's been very useful so far to me and has saved me many hours of work. I could have written it all in Python if necessary, but it would have taken me weeks for what would be a few days of work.

My advice is to just download it and use it. The documentation is pretty good. There are many good videos online for it. If you go to YouTube, you can get pages and pages of KNIME tutorials. They're pretty clear, and they are produced by people who've used it. It's not just company advertising, as far as I can see.

I'm a professor at the local university. So, I used it to train virtual students in mechanical engineering.

I'm training a class for mechanical engineers on factory utilization and the basics of data science. That's what I use it for.

It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME.

In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. 

Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them.

I have been using it for four years. 

I've never had any problems with it, so it's a ten out of ten.

I would rate the scalability a nine out of ten. For a basic training course, it's still fine. But I'm not a professional in using KNIME.

I used RapidMiner. I have not been using it in six years. I used to use it six years ago. Then I switched to KNIME because a lot of my colleagues are using KNIME, so it felt like the right way to do it.  

Moreover, I switched from one university to another, and at my new university, other colleagues are using KNIME as well. So, for the students, it's easier to go just with one product.

Overall, it's still easier than using Python, so it's still fine. But, actually, they made it more complex by switching from the last version to the one before.

We're using the free academic license just locally. I went for KNIME because they have a free academic license. And to be honest, I never bothered to check the prices.

I like it a lot. I would advise that you shouldn't be afraid of data science. It's actually straightforward.

Overall, I would rate the solution a nine out of ten. 

As a university professor instructing courses on data mining and machine learning, I incorporate both KNIME and another software application into my teaching. This approach allows me to demonstrate various use cases effectively. I actively engage my students by having them utilize both software applications, providing practical hands-on experience in the areas of data mining and machine learning.

The most valuable is the ability to seamlessly connect operators without the need for extensive programming.

To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages.

I have been using it for more than ten years.

I would rate its stability capabilities nine out of ten.

It provides good scalability abilities, I would rate it eight out of ten. Currently, more than sixty individuals use it on a daily basis.

They are helpful and I am highly satisfied with their customer support services. I would rate it nine out of ten.

Positive

We use Orange as well.

The initial setup is straightforward.

While there are certain limitations in functionality, you can still utilize it efficiently free of charge.

I would recommend it, especially for those who prefer not to program or have limited coding intervention. Overall, I would rate it nine out of ten.