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.

We use KNIME for tax technology. We want to implement technology in our tax domain.

KNIME is very fast and scalable. There are a lot of connectors available in KNIME.

KNIME is less secure than Alteryx. KNIME's documentation is not strong. I cannot make good documentation on a KNIME workflow like in Alteryx. Alteryx has more color options where I can put tools into different containers and write some annotations. I felt that was missing in KNIME.

I have worked with KNIME for one year.

KNIME is a stable solution. However, you have to be mindful while working with any open software because it's open to anything.

KNIME is very easy to scale because it's an open source solution. There are a lot of professionals who continuously put some effort into introducing scripts because it is easy to integrate with other technologies. So the add-ons are easily available for KNIME.

KNIME's customer support is good. In three and a half years, I found solutions to 99% of my questions.

KNIME's installation is very easy. All you have to do is log on to the website, check for the latest version, and hit the download button to get it.

KNIME's deployment takes around five to ten minutes. Maintaining the designer version of KNIME is more or less the same as maintaining Alteryx.

KNIME is a cost-effective solution because it’s free of cost.

Small, medium, or enterprise businesses can use KNIME. You have to be more careful in downloading because it is an open-source solution, and anybody can even spread a virus. It's up to the users whether they want to take that risk. But I don't see such problems working with the Alteryx community, where all the information is much safe to download and upload.

I would suggest KNIME to someone with a low budget looking for a cost-effective solution. However, I would also give a disclaimer that they should be careful while downloading the connectors from the KNIME community because it's more open. Since it is an open-source solution, there are high chances of having some security issues.

Overall, I rate KNIME seven out of ten.

I am an intern. I am pursuing my master’s degree. I use this solution to propose a solution for accreditation review. I needed a tool to automate this task for my sources. This solution has helped me to do that.

The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes. So maybe it would be helpful to have a way to find similar column names automatically and execute a single approval, for instance.

In future releases, I can suggest having an API along with tools or services. This would allow for customization since the API is already available in other versions.

So, I suggest having an API for customization.

I have been using KNIME for six months.

I would rate the stability of KNIME a ten out of ten.

I would rate the scalability of KNIME a six out of ten.

I have used Alteryx. I prefer this solution over Alteryx because of the pricing point. KNIME is free and open source.

The initial setup is easy. I will rate my experience with the initial setup a nine out of ten, where one means it's difficult to set up, while ten means it's very easy.

I deployed the solution myself. It took around one month to deploy it.

KNIME is free and open source. I would rate the pricing model one out of ten, where one is low price, and ten is high price.

It is a good tool and easy to learn. We do not need to know the codes before using the solution.

Overall, I would rate the solution a seven 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.

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.