KNIME Business Hub Reviews

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.

I use KNIME for analysis-related purposes. I am currently in the process of developing some models for analysis.

The most valuable feature of the solution stems from the fact that it is a user-friendly tool where a person doesn't have to get involved with codes since you just need to drag the nodes to create your model, which is a very easy process for me.

The most difficult part of the solution revolves around its areas concerning machine learning and deep learning. The aforementioned area can be considered for improvement.

I have been using KNIME since 2019. I am an end user of the solution.

It is a stable solution.

It is a scalable solution.

I am the only user of the solution in my company. I do provide training to other employees in my company on how to use KNIME.

I have experience with Excel, and I faced some limitations since my company had loads of data to analyze. Considering that my company had loads of data to analyze, I would say I find KNIME to be very useful.

My company has some problems related to the solution's updates. I don't know if there are some restrictions from my organization because of which I cannot update or install some extensions.

The solution can be deployed in a few minutes.

The solution is currently deployed only on my personal computer, which I use in my company.

Only one person or an IT administrator is required to take care of the installation phase of the product.

KNIME is a cheap product. I currently use KNIME's open-source version.

I have experience with Python. Compared to Python, KNIME is better because of the user-friendliness it provides. With KNIME, you don't have to get involved with codes. KNIME provides nodes, making it a very easy tool to use.

I have not received any response from my company, though I had proposed to my organization to buy KNIME so that we can use it on the servers since, right now, it is like a standalone tool used on my personal computer only. I am just a basic and not an advanced user of KNIME. I find KNIME to be a very useful tool.

Speaking about the maintenance phase of the product, I would like to say that I cannot update the solution. If a new version is released, I cannot update the product. I always have to request my organization and the IT team to download and install the product's new version for me.

I recommend others to use KNIME. I have recommended KNIME to my colleagues.

I rate the overall solution 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.

It's mostly data preprocessing, handling, and processing (ETL) processes, as well as expanding the transport load. 

Additionally, we also work on various machine learning tasks, such as regression models and other small topics related to machine learning.

I've tried to utilize KNIME to the fullest extent possible to replace Excel. Our company has been heavily reliant on Excel for generating reports and performing data transformations. With KNIME, I've been able to combine data from Excel, SQL Server, and various other resources efficiently.

There are a few aspects that I am not entirely satisfied with. For instance, when integrating KNIME with our SAP system ERP and HANA, it's not as straightforward as expected. We need to find alternative connectors like the Teradata connector, which adds complexity.

So far, I've had some problems integrating KNIME with other solutions. Thus, it could be an area of improvement. 

We have been using KNIME for two years.

Overall, the product has been stable. It has efficiently handled the tasks we have encountered so far.

There are two end-users using KNIME in our organization. Because we are still beginners, we are only using it to learn how it works and get a better understanding of the system. We are not yet certain if we will use it extensively for all topics.

The initial setup was easy. 

I deployed the solution myself. 

We use the free version only. 

We are working with KNIME on some small projects, but we are also looking for an alternative solution to explore.

Overall, I would rate KNIME a seven out of ten because we faced a problem with the integration with other products, like SAP.

It's for big data or descriptive analytics involving data manipulation, formatting, and formulas.

Automation is most valuable. It allows me to automatically download information from different sources, and once I create a workflow, I can apply it anytime I want. So, there is efficiency at the same time.

It's like an Excel on drugs. It's more powerful than Excel, and it allows me to do macros easily.

I downloaded KNIME myself, and it's for self-learning. It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge.

I've been using this solution for about three years. I last used it in my previous company about two months ago. We are not using it in my current company, but I'm using it for self-learning.

It's stable. I'd rate it a nine out of ten in terms of stability because when I load huge data, it sometimes takes a while and crashes. If I don't load it much, it works fine, but if I overload it, it crashes.

It seems scalable.

It involved just downloading the app from the web. I didn't have any interaction with them. I just downloaded the free version.

They have different versions, but I am using the open-source one.

I'd recommend it, but it comes with a trade-off that you need to spend a lot of time on your own to understand how it works. It's user-friendly, but the fact is that I downloaded it by myself, so I didn't have any formal support on how to use it. It was used in my previous company. They had the license and they encouraged us to use it. That's how I know it.

I'd rate it an eight out of ten because I am not able to do some of the things, which could be because of my lack of knowledge, but it's a very good product. I see the benefit in terms of efficiency.

I'm a professor, and I learned about KNIME from a data science course. I use KNIME for data visualization, manipulation, and generation.

What I like most about KNIME is that it's user-friendly. It's a low-code, no-code tool, so students don't need coding knowledge. You can make use of different kinds of nodes. KNIME even has a good description of each node.

It's also helpful to learn more about the different concepts in KNIME, such as data mining, neural networks, and other visualization nodes for generation. You can gain a lot of insight from the tool as a data scientist.

Though I can use KNIME in a 64-bit platform in the lab, it's missing some features. For example, from my laptop, I can use the image reader feature of KNIME. However, in the lab, the image reader node is missing. This is an area for improvement because not all nodes appear in the 64-bit system. In other systems, you get to use all nodes or features. It should be uniform in all systems, though I have no idea if it's a software problem or a corruption in the system that's in the college lab. At home, I can see and access the image reader node, but in school, that node is missing.

What I want to be added to KNIME is the feature of extracting data from social media platforms, whether structured or unstructured and for that data to be automatically converted into a CSV file that I can use. I want a data cleaning and collection process from KNIME that works for social media platforms because datasets in social media can serve as business intelligence or can aid businesses. Social media is the trend nowadays, so I want a KNIME node to analyze data from social media platforms.

I'd been using KNIME for three months now.

In my three months of using KNIME, its stability has been okay, but I want to explore the tool more. Stability-wise, it's a seven out of ten. When the network is stable, then KNIME is okay to use.

KNIME is a scalable product, so I'm rating it as nine in terms of scalability.

I've not used any other tool besides KNIME, but I'm planning to try RapidMiner, which I've not yet installed.

KNIME was easy to set up, so it's a ten out of ten, setup-wise.

KNIME is an open-source tool, so it's free to use.

At the moment, only ten students use KNIME. The course just started.

In my three months of experience with KNIME, it's an eight out of ten.

We are using KNIME for price prediction, privacy missions, the commander model, ETL, and a couple of algorithms we've developed.

One of the greatest advantages of KNIME is that it can be used by those without any coding experience. Even those with no coding background can use it. 

KNIME can be used by people without coding experience. It can be used by people who don't have an IT background and don't have coding knowledge. This is different from Python or R, which require coding experience to use.

When deploying models on a regular system, it works fine. However, when accuracy is a priority, hyperparameter tuning is necessary. Currently, KNIME doesn't have the best tools for this which they could improve in this area.

I have been using KNIME for approximately one month.

I'm not sure if it is stable, we'll have to see how KNIME performs with larger amounts of data, as I have heard it is not very reliable. With smaller data sets, however, it seems to be stable.

We will use this solution more in the future when we do not need people with coding experience.

We have two people who are using this solution in my organization.

I have not used the support from KNIME.

I used the open-source package and started experimenting with it in Python, R, and KNIME. For KNIME, I had to go through the KNIME forum for troubleshooting. I didn't get a response for any of the issues I encountered on the KNIME forum. As for other open-source languages, I haven't received a response for any of the issues I faced either.

The initial setup of KNIME is easy. It can be done with the interface within half an hour.

I had taken some online courses and read about KNIME, and I wanted to try out a drag-and-drop software. I was interested in evaluating KNIME, and this is why I am using it.

My advice to those who are new to data science and don't have any coding experience would be to use KNIME, along with some other programming languages. KNIME is great for creating visualizations and dashboards, and I have advised a few of my colleagues to use it for their own projects.

I rate KNIME a seven out of ten.