KNIME Reviews

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 primarily use the solution as an ETL - to Extract, Transform, Load topics.

All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function.

Technical support has been extremely responsive so far.

The solution has a very strong and supportive community that shares information and helps each other troubleshoot.

The solution is very stable.

The initial setup is pretty simple and straightforward.

The user interface could be a little bit more comfortable. The usability, in general, could be much better.

The predefined workflows could use a bit of improvement.

I've been using the solution for five years.

The solution is pretty stable. I don't recall facing any bugs or glitches. The solution doesn't crash or freeze. It's fairly reliable overall.

We have about 10 users in the company that are on the solution regularly. They are all either engineers or business consultants.

The technical support on offer is very good. We rarely have issues, however, if we do, the support that is provided is very fast and the team is very knowledgeable. There's also a very strong and active KNIME community that includes KNIME developers.

At the end of the day, when we run into issues, it's dealt with immediately and we're very satisfied with the level of service provided.

The initial setup isn't complex. I'd describe it as straightforward. What makes it very easy is that there's so much material available online. Users have access to many forms of documentation, including webinars and training documents. When it comes to finer details, of course, it gets more complex, however, that's pretty normal in every product. 

We are KNIME partners and also provide KNIME training to clients as well.

We're a startup, and we're in the startup phase, however, we are working to build more business relationships with KNIME.

In my previous company, we were also partnered with KNIME. Currently, I'm working as a consultant and we have a customer who is using SAP Analytics, however, I am interested in learning more about the differences between KNIME and SAP Analytics. That's something I'm looking at right now.

We would definitely recommend this solution to other organizations.

I'd rate the solution eight out of ten. I don't know if there's a tool better than this particular product, however, there's always room for improvement on any technology.

This solution is primarily used for various data analytics in an enterprise environment. 

The reality of any data analytics project including Data Science is that 90% of the effort goes into data sourcing and preparation. Data usually comes from multiple sources including data warehouses, web scraping, Excel input, free text, etc. KNIME allows you to do the 90% plus other predictive functionality.

It is a free open-source tool that performs very similarly to other expensive tools. KNIME has been great for me over the years. It allows me to connect to various sources including data warehouses, then put the processing logic together (ETL-like), which can be quite complex and produce the required output. Ultimately, it would go into Excel or Tableau for presentation.

The features that I find most valuable are:

1. The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes.
2. Unlimited volume of data; you are only limited by the machine you run on.
3. Python and R integration.
4. Predictive functionality and text analytics. If it is not enough then you can use custom Python and R scripts.
5. Looping functionality.
6. Variables allow you to parameterize your flows.
7. Run one node at a time, which is something that Alteryx users dream of doing.
8. Managing (collapsing) sub-flows, which is another thing that Alteryx container users also dream of.

The areas that I feel need improvement are:

• It needs support for a joiner node to have three outputs (left unmatched, matched, right unmatched), as competitors do (have not checked 2019/20 releases).
• I need the ability to add additional comparison conditions to a join. For example, in SQL you can specify only rows with a date fitting within a date range from the joined file. At the moment in KNIME, you should allow a join explosion to take place and filter what you need later, but sometimes the output becomes too big.
• It would be helpful to have more examples of Java code for nodes, like Java Snippet.
• I would like to have this solution show row counts on canvas, as it would improve the control and speed to build the workflow.
• The pseudo-code types could be rationalised into one (e.g. only Java).
• I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script.

I have been using KNIME for between four and five years.

My system occasionally may crash like other similar tools, although autosave is available.

Scalability is limited to a desktop application.

Obviously, as an open-source application, your options are limited but I have found answers on forums when I needed help.

Recently I have been using Alteryx so I have collected a few points on differences in both tools. Both are good, I can conclusively say I could go back to KNIME and be as effective data professional as I am with Alteryx.

I have to use Alteryx due to my client's tool choice, but I know that what I am doing with Alteryx right now could be done better in KNIME. Of course, Alteryx has its own advantages for certain areas.

It is a relatively simple install. You can even avoid installing it and run from a directory.

KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website. The fact that KNIME is open source may create challenges from an IT security view in an enterprise environment.

For this review, I would include Alteryx and Lavastorm (the latter is no longer available).

If you need a good Visualisation functionality, you should use Tableau or something of that caliber. However, the data prep can be done KNIME, which would give you extra confidence that what goes into your Visualisation layer is correct.

Overall, KNIME is definitely worth considering.

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've been using some of the analytics and photo manipulation features. I'm a teacher, and I've been using KNIME in my university.

It's a huge tool with machine learning features as well.

There are some parameters that I would like to have at a bigger scale. The upper limit of one node that tries to find spots or areas in photos was too small for us. It would need to be bigger.

I've been using this solution for two and a half years.

Overall. it's stable. However, every time there is a major version release, we end up having to upload many of the nodes again. It takes some time to do the version change.

It is scalable, especially with a server.

The technical support is excellent, fast, accurate, and friendly.

I used Power BI, but when I found KNIME, there was no going back.

The initial setup is easy.

You should do the KNIME courses, particularly the first two courses.
They would give you a very good base to make KNIME work nicely, and you would also know how it really works.

It is a really good system, and on a scale from one to ten, I would rate it at ten.

Some of the projects that require KNIME are related to sales or the supply chain. We use it to aggregate data from diverse sources rather than predictive analytics. It's primarily for data collection, management, and preparation.

KNIME is fast and the visualization provides a lot of clarity. It clarifies your thinking because you can see what's going on with your data.

We find KNIME useful and convenient for data preparation.

KNIME's licensing and data management aren't as straightforward relative to Alteryx. Alteryx's tools are more sophisticated, so you need fewer to use it compared to KNIME. I think tab implementation could be easier, too.

My company has been using KNIME for about a year and a half. I don't work on the solution personally, but our analysts do. We use it off and on, depending on the project. It's not every day that we have projects requiring that, but we use it when we do.

KNIME is stable. We haven't seen any issues.

I think KNIME is scalable, but we haven't had a project that would test that sort of thing. The projects requiring KNIME aren't on a huge scale. We haven't tried to scale it, but I think it should be okay. 

My team has used the forum, but we never contacted support for any specific deployment query. I think the online documentation and training are quite sufficient.

We worked with Alteryx, but we switched to KNIME because that's what our customers wanted. Alteryx is a good solution, but the cost was exorbitant for the client. 

Setting up KNIME isn't hard. When we first installed the solution, it took one or two weeks to understand the various features. After doing it a few times, it didn't take as long. It only takes one or two people to deploy.

The ROI would depend on how KNIME is being used. Some projects would be more economically viable and save a lot of money. Others wouldn't save much.

KNIME could be cheaper, but it's okay compared to other solutions. Our projects are primarily within India, and it is extremely price-sensitive here. If we were doing projects in the UK or the US, it probably wouldn't matter.

I rate KNIME nine out of 10. I would recommend it. I think KNIME is an excellent solution.

The solution allows one to do many things, including data preparation. I was able to apply basic algorithms through just dragging and dropping. This in contrast to Python and other solutions, which involve much coding. 

I would prefer to have more connectivity. The user documentation is insufficient. I would like to see more enterprise level application. There are high end features which should appear, the MLOps platform being one. This feature is key. 

There should be better connectivity to such platforms as AWS and SageMaker, as we rely heavily on AWS in RL. For certain South Asian markets, we plan to go with Azure, so it is important to have connectivity to both of the major clouds.  There should be AI machine learning based algorithms. Such features should be available out of the box with good precision. 

I have worked with KNIME for a couple of months. 

The solution is stable.

KNIME assets are stand alone, as the solution is open source. I have not looked into their enterprise level application costs. While cost is a parameter, I would definitely consider other options which provide value for one's money. 

The solution is good for small scale implementation. Other solutions should be considered for enterprise level implementation. 

I rate KNIME as a five or six out of ten. 

The solution is good for teaching, since there is no need to code. When teaching, the work flow, the process, is easy to explain to students. They understand that there are four steps in data mining on the machine learning project. Data must be injected and cleaned, after which engineering features must be made. Finally, the model must be created and deployed. Both KNIME and RapidMiner have these capabilities.

An improvement which can universally be made to products is to make them more simple. Code-less products are simplified. Both RapidMiner and KNIME should be made easier to use in the field of deep learning. 

While KNIME has all the requisite features, there is a shift from coding to programming with virtual language. It is only a process of making one's solution easier to use. 

I have been using KNIME for more than 14 years. 

The solution is good and they have made improvements over the last version. The same holds true for RapidMiner. Both products are very good. 

The best way to use the solution is to dive in, get practice and use it more. 

I rate KNIME as a nine out of ten.