At our company, we provide consultation services to federal institutions, and in some cases, we recommend KNIME to clients and have also implemented the solution for a few projects.

The graphic features of KNIME need improvement, especially when working on dashboards.

I have been using KNIME for three years.

The stability of KNIME depends on the resources on the computer in which it's installed. KNIME requires a high capacity of RAM and memory; if the hardware needs are fulfilled, then the product shows satisfying stability.

When I used to work on KNIME every day, I faced some issues, but they were quickly resolved with the help I received from the active KNIME community.

Our organization has about ten KNIME users, and the solution is used around once a month.

I would rate tech support a nine out of ten.

I have experience in Qlik Sense, Tableau, and native Python programming. I personally switched to KNIME because my present employer had KNIME already implemented in the company.

The initial setup of the solution is easy. Installing the KNIME Analytics free version on a desktop takes only a few minutes. The setup and installation process is quite easy to begin.

The implementation was carried out in-house. The solution is free to use; therefore, a partner or reseller is not needed. For the KNIME Business Hub, our organization is directly communicating with KNIME, and a reseller isn't required.

KNIME offers a free version to use. KNIME also has a server version with almost the same price as competitors.

In data analytics workflows at our company, we use KNIME for the preprocessing of data, and for a few projects, an ETL track was built using KNIME. We are planning to use KNIME Business Hub to create data apps where a dashboard of analysis can be prepared and presented to others.

Our company mostly uses KNIME for small datasets, projects, and customers who don't have much expertise in data analysis. We introduce KNIME to our organization's customers to get started with data analysis. The solution is very easy to use. For advanced data analytics, I use a tool different from KNIME.

The product has impressive data-blending capabilities. The solution is user-friendly and helps our company easily onboard new users. I would definitely recommend the product to others, majorly to individuals with beginner-level programming and data analytics skills. I would rate KNIME an eight out of ten.

I use KNIME for a wide variety of purposes. Most recently, I employed it in a survey and experiment to explore different teaching methods, specifically using software to help nurses learn drug dose calculations. This became particularly important during the pandemic when there was an urgent need to reskill healthcare professionals rapidly.

During the early days of the pandemic, no one knew how to treat COVID-19, and it was a dire situation with high mortality rates. The healthcare system had to adapt quickly, with retired nurses and doctors being called back into service and nursing students stepping up to meet the demand. A program I had previously worked on, which taught drug dose calculations, became crucial again. Nurses had to familiarize themselves with a whole new set of drugs.

I analyzed the data from this program using KNIME. I handled various statistical analyses, including mean, standard deviations, regression, correlation, and Wilcoxon signed-rank tests.

It's difficult to pinpoint one single feature because KNIME has so many. For starters, it's very easy to learn. You can get started with just a one-hour video. The drag-and-drop interface makes it user-friendly. For example, if you want to read an Excel file, drag the "read Excel file" node from the repository, configure it by specifying the file location, and run it. This gives you a table with all your data.

Next, you can clean the data by handling missing values, selecting specific columns you want to analyze, and then proceeding with your analysis, such as regression or correlation. KNIME has over 4,500 nodes available for download.

In addition, KNIME offers various extensions. For instance, the text processing extension allows you to process text data efficiently. It's more powerful than other tools like NVivo and Palantir.

KNIME also has AI capabilities. If you're unsure about the next step, the AI assistant can suggest the most frequently used nodes based on your previous work.

Another valuable feature is the integration with Python. If you need to perform a task that requires Python, you can simply add a Python node, write the necessary code,

The hardest part is keeping a tidy workspace because of the many nodes involved. When teaching, it would be helpful if there was more emphasis on how to group nodes effectively. For example, turning frequently used nodes into a single component can simplify things.

I have been using the product for three to four years.

When I had to do things new to me and didn't know how to proceed, I used the KNIME hub and community forum. Both times, I received answers within twelve hours that solved my problems. From that experience, I'd say their support is superb, even on the free version, and I imagine it's even better on the paid version.

I use the solution's free version.

The tool has a community, and people are very helpful. I've been impressed by that. I'm trying to think of ways I want to improve. I did start it up on a Windows PC. It seemed a bit easier for some reason, but I'm now on a Mac. I have trouble with the magic mouse, but that's an issue with any software not specific to KNIME. It worked well when I received data from other people. I haven't tested it on a ten-billion-item database, but it works well for hundreds of thousands to a million items. I haven't tried the big databases because that's for the commercial version.

The solution has a lot of video material for teaching and upskilling people. If I've got a colleague that I just introduced to it, I send them links to two videos, and they learn from that. That's two hours of work for them in two sessions, and they've got a programming language under their belt.

For instance, I've never used it to build a neural net, though it has many different neural nets. I have used it for decision trees, regression models, and so on, and KNIME has been very easy to use for those. It takes much of the work away from you. For example, if I build a decision tree, I usually want to take several samples from your training data and then choose the best outcome.

With the tool, you just put the Decision Tree Learner node in there, set what percentage you want for training and testing, say 60% for learning and 40% for testing, and how many times you want to do it, like seven times. It runs seven samples, does the training and testing, and reports back to produce a model. Then, you can use a Scorer node to score your results. I produced the first-ever machine learning exercise in nursing in 2008, which wasn't using KNIME, but I think KNIME was available then. That project was associated with a reduction in the dropout rate from the university from 19% to five percent over three years. And you can do all that for free with KNIME, even with a dataset of about 1,200 students, on your PC with no trouble.

I read a report from Uppsala in Sweden where, during the pandemic, they had to repurpose many drugs developed for other diseases to find out which ones would be useful for COVID. They went through thousands and millions of drugs, and it seemed to reduce the time to develop or choose drugs by a major factor.

The solution accessed about eight or ten different chemical databases, biobanks, and similar resources and could integrate with all of them. One of the good things about KNIME is its ability to read different data sources. For instance, it has platform connectors, allowing you to link to various external systems. It's just a matter of plugging in a node and linking it up without usually needing to write new code. For example, in my use case, reading from an Excel file used to require configuring a node manually, but now you choose the file you want to analyze, put it on your workspace, and it recognizes the file structure automatically, whether it's an Excel file, CSV, or tab-delimited file, and provides the correct node. It's quite intelligent.

If you’re considering using the solution for the first time, I recommend starting with a one-hour introductory video to get a basic understanding. If you need more information, check out KNIME TV. They have a channel with hundreds of short videos, usually five to 30 minutes long. These videos show you how to drag and drop nodes, configure them, and run them to see the results. It’s a very visual way of learning.

I rate the overall product a nine out of ten.

I use KNIME to simplify the modeling process.

The tool's analytic capabilities are good.

I wish there were more video training resources for KNIME. The current videos are very short, and most learning is text-based. Longer training sessions would be helpful, especially for complex flowchart use cases. Webinars focusing on starting projects and analyzing data would also be beneficial.

The solution is scalable.

I haven't contacted the tool's support yet.

The tool's deployment is easy.

I use the tool's free version.

It takes some time to get familiar with it. I'm not sure how long it will take in the meantime. If one person learns it but the whole institution doesn't use it, that's a problem. Some people in our department use QuickSight, I use Tableau. We speak different languages, and it's hard for us to work together. Some use KNIME. We use it and then stop. We switched to Tableau, but it's expensive, so they're trying QuickSight. I don't know which platform we'll end up using.

We're still exploring KNIME for data manipulation, though Tableau or Power BI might be more convenient. I've used Alteryx before, and KNIME seems similar. I mainly use KNIME for machine learning, not as much for data manipulation.

I rate the overall product a seven 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.

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.

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.

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.

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 encountered a problem that I managed to resolve effectively. I documented the issue in a paper and aimed to determine if the issue was due to normal network behavior or an anomaly. To investigate, I employed machine learning models and used the KNIME’s database. I gathered a significant amount of data and extensively applied machine learning models. Ultimately, I achieved improved data accuracy, especially in the context of network data.

I believe that some individuals may not be skilled programmers, and this is where the agenda comes in. It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea.

The pricing needs improvement.

I have been using KNIME for the past six months.

It is stable solution.

It is scalable solution.

The initial setup is straightforward.

It is expensive to procure the license.

I would rate the overall solution an eight out of ten. I would suggest this application to individuals involved in auditing. It's user-friendly and makes it easy to initiate model creation.

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.

KNIME is an excellent product, and I've used many other platforms like Google Collab, Azure, and even AWS. However, KNIME, especially for AI and machine learning, is very different. It's almost no-code. You can add code if needed, but it's not necessary.

KNIME has hundreds, maybe even thousands of modules, which are called nodes. These nodes, along with their libraries, are essential for solving specific issues or problems. You can select the nodes you need, and they come pre-recorded as visual boxes. You just need to assemble the nodes required for your solution. As mentioned earlier, you can search for libraries and select the appropriate nodes, then combine them to form your entire workflow. KNIME supports coding in Python and other languages, but you can assemble the nodes visually without writing code. Each node has a specific function, and if one node doesn't suit your needs, you can easily replace it with a different one.

Additionally, each node has inputs and outputs, and you can configure them based on your requirements. Once the nodes are set up, you can attach the data and let it flow through the nodes to execute your workflow.

One significant improvement is its speed. With KNIME, you can accomplish many tasks in a single day. It's very fast since you mostly work with prebuilt nodes and libraries. Also, the latest version allows us to add Python code if needed.

There are several valuable features. First, it's a free product. Second, its speed due to the no-code approach. And third, its a comprehensive library of nodes that covers almost anything you need.

One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well.

However, if you encounter very complex requirements, you might need to add custom code to achieve your desired outcomes. This is an area that could use some improvement, but the advantage is that it encourages you to evaluate and minimize coding efforts. As a result, you can reduce the overall amount of coding required, which is a positive aspect of KNIME.

Another area that could be improved is related to the libraries. While they are quite extensive, they might not always match your exact needs. In such cases, you might have to do some coding to tailor the solution accordingly.

Therefore, one area for improvement is the flexibility of prebuilt nodes, as they may not always match complex needs perfectly. Also, enhancing clarity on what the nodes do would be beneficial.

For additional features, there are a couple of things that come to mind. Firstly, it would be great to have more clarity on what each node does. Sometimes, it's not very apparent, and additional information would be helpful.

Secondly, it would be beneficial to have better ways to interact with and manage nodes, enhancing the user experience.

And finally, I think KNIME could improve on how easily it allows for extending functionalities with custom code. Although it's relatively straightforward now, making it even more accessible would be advantageous.

We have been using KNIME for two years. We currently use the latest version.

Stability is excellent. I would give it a nine out of ten.

As for the on-prem version, I would rate the scalability around a seven out of ten because it's definitely scalable, but we haven't really pushed it to its limits.

KNIME provides good support. The only challenge is that they are in Germany, so sometimes the time difference can be a factor. As it's a free product, they may not be available all the time. But the platform itself is easy to use, and they have very good documentation, so we rarely need technical support.

The deployment is not very hard or time-consuming on-premises. The only challenge is dealing with hardware limitations like memory and GPUs.

Currently, we deploy KNIME on-premises, but there is a paid cloud option available.

We have seen an ROI. In my case, as a consultant, I can create proofs of concept very quickly using KNIME. For example, if a client wants to explore a specific idea but is already committed to using platforms like Azure, Google Analytics, or AWS, we can still use KNIME to demonstrate the concept. This allows us to try out new ideas and algorithms before implementing the full project on their chosen platform, such as AWS, if needed.

The proof of concept approach is especially helpful when clients need to validate the feasibility of certain algorithms or machine learning techniques.

The price for the cloud version is very reasonable compared to other products at the same scale. If you expand to the same scale, KNIME could be a more cost-effective option.

If you're evaluating KNIME, make sure to use a comprehensive use case. Sometimes, users might not find the nodes they need in the libraries, but most likely, it's due to improper searching. KNIME offers a unique platform with a wide range of nodes, so thorough exploration is essential to fully benefit from its capabilities.

Overall, I would rate the solution a seven out of ten because I have not yet tried every feature. Otherwise, KNIME is really a great product.