KNIME Business Hub Reviews

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.

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 the solution for data analytics and logic design.

The product is working fine with Oracle.

It is is written in Java. If they can output the Javascript, it will be much better. Also, it could be integrated with Visual Studio.

I have been using KNIME for three years.

20 users are using this solution. Scalability is quite easy, but handling many notes can become messy.

Most of the things is available in the community.

It's quite easy to setup.

I have a CSV reader. When I reset that CS reader, and It gave some error.

I have a CSV reader, and I encounter an error whenever I try to save. However, if I reset the CSV reader, I am able to save successfully. It’s a rare issue, but there's something wrong with the CSV reader. The error message doesn't provide a solution, only indicating a problem with the CSV reader.

I want to save the project but always face saving issues. If I reset the node, the saving works fine. The error message isn’t clear about what is wrong or how to fix it. I discovered on my own that resetting the CSV reader from green to yellow allows me to save the project. This issue is quite rare.

Last Friday, there was a widespread CrowdStrike issue, and I had to restart my computer. After restarting, I lost my entire project.

I recommend the solution.

Overall, I rate the solution a 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 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. 

KNIME is mainly used for developing workflows for machine learning and VA. We are using integrated Python scripts.

The solution has improved our company because it is economical as it is an open-source platform. There are not a lot of costs involved.

The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way.

I want to access more of the popular deep learning libraries or frameworks as they have in other solutions, such as TensorFlow and PyTorch. There should be better integration, and a better way to use those libraries because they are very popular. Our company uses PyTorch and TensorFlow regularly to solve many computer vision problems. We expect better integration of those two tools.

KNIME could improve when it comes to large data markets.

I have been using KNIME for approximately one and a half years.

The solution is stable but it could improve.

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

We have approximately 25 people using this solution in my company. The employees typically do business analytics with MLS and DLLs in their workflow.

We are not using the solution at its full capacity.

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

I have not contacted the support from KNIME directly. We use online forums and blogs.

We used Alteryx prior to KNIME.

We do the deployment of the solution in-house. We have a team of approximately five to seven people who not only take care of these pipelines, and workflows in this solution, but also do a lot of AI and ML module developments.

This is a free open-source solution.

We selected KNIME rather than Alteryx because of the price comparison.

We usually only use the coding methods using Python. When people don't have much experience with coding, then we always recommend they choose the concepts of analytics and machine learning, mainly a no-code tool, such as KNIME or Alteryx. They become close to citizen developers. We will teach them coding aspects later. They always start with a no-code or a low-code tool, such as KNIME and Alteryx. Once they learn slowly, we switch them over to coding access.

I would recommend this solution for use cases that has a small amount of data and not a lot of code needed.

I rate KNIME a six out of ten.

We use this solution primarily for automation processing, source data, and for data lake or databases. 

I like the visual features and that we can connect using a pixel or an icon for the sources and controls. KNIME is easy to use, it's stable, powerful, and has a lot of features.

For now, the license is quite expensive for us and it would be helpful if that was reduced.

I've been using this solution for a few years. 

The solution is stable. 

We have five people using this solution so we haven't yet tested the scalability. 

We use the community for any questions we have. 

I think the deployment requires a specialist but once it's implemented it doesn't need any maintenance. 

We use the community version; the enterprise version is expensive for us. If we need better support or have other requirements we might need to move to the enterprise version. 

I rate this product nine out of 10. 

We use KNIME for data and manipulation.

We have found KNIME valuable when it comes to its visualization.

We have been using KNIME for the past five years.

KNIME is stable for our needs after using it for five years.

KNIME is not scalable.

I have had no need to contact technical support.

I have used Python. The logic is the same, but the interface is totally different.

The setup for KNIME is simple. I would rate the setup a five on a scale of one to five.

I would rate KNIME a seven on a scale of one to ten.