KNIME Reviews

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.

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 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.

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.

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.

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 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.

We used the solution for data analysis. With the help of its graphical workflow interface, we were able to identify the exact logic behind the source code.

The solution is the best data science tool if you gain in-depth knowledge about its functions.

They should improve the solution's integration with other platforms. Also, they should add more functions to its server.

We have used the solution for less than a year.

I rate the solution's stability as a seven as we face memory leakage issues.

We have nine users of the solution in our organization. I rate its scalability as a nine, as we can deploy it in a cluster.

We used Rapidminer earlier. We switched to KNIME as it is a better tool for data mining.

The solution's initial setup is easy. The process takes several minutes to complete with a good network connection.

We have a technology tool to help us install the solution.

You must have the essential knowledge to solve the solution's compliance issues. It could be easier to use and integrate with other products.

I recommend the solution to others and rate it as a seven.