KNIME Business Hub Reviews

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.

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.

Positive

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 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 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.

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. 

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.

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.