KNIME Reviews

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.

Knime is used for data analytics.

It can handle an unlimited amount of data, which is the advantage of using Knime.

It already has algorithms included.

I haven't had a lot of time to explore Knime in detail, but when you compare it with Orange, I would like it to be able to find data and collect it from another source. Also, to collect data for Knime from Twitter, Instagram, or Facebook for example, and to add widgets to Knime.

It could input more data acquisitions from other sources and it is difficult to combine with Python. It can be done with special requirements.

I have been using Knime for three months.

In the three months that I have been using Knime, it has been very stable.

From my understanding, it is scalable. It can handle a large amount of data. It indicates that it can handle unlimited amounts of data.

The initial setup was straightforward. It was very easy.

This is an open-source solution that is free to use.

I would recommend Knime to others who are interested in using it.

Students can use Kmine for their research.

I would rate Knime an eight out of ten.

The data analytics capabilities in KNIME are excellent. It's not just a statistical ETL tool. We can go deeper and do various types of tasks beyond straight analytics.

The product is open-source and therefore free to use.

The solution offers lots of different options.

The user interface could be a bit better. It's currently very dated.

They should look at other vendors like Alteryx that are more user friendly and modern. 

From a systems point of view, the tool is not completely user friendly.

Users tell us they would like to do their own analytics and find it difficult to accomplish without the help of a technical service.

You need to be a bit more knowledgeable in order to handle the solution. It's not difficult, it's just more technical than other options.

I've been dealing with the solution for four years.

The solution is very stable. There aren't issues surrounding bugs or glitches. It doesn't crash or freeze. It's quite reliable.

It depends on the requirements you have, however it is scalable, at least for the next two years.

We typically work with enterprise-level organizations. The companies aren't that small.

The technical support is okay. I'd give them three out of five stars.

I don't find any of their online tutorials help anyone at all. I am comparing KNIME with Alteryx mainly due to the fact that those two are the main ETL tools which most of my clients use. The technical support and documentation that are available for Alteryx are quite good. We don't get that level of documentation or videos from KNIME's support. It's very limited. 

We also use Alteryx. We use both solutions, depending on the client. I tend to recommend Alteryx. For someone who has good technical knowledge, they can go with KNIME. However, if they're not a techy person, I would recommend Alteryx for them.

The initial setup is not complex. It is pretty easy. However, you have to know what to do. If you have software demo documents or if you have tutorials to support you, then it is easy. I wouldn't say that it's a complex tool at all. It's pretty easy.

The solution is open-source and therefore cheap to use. Anyone can access it. They can just download it off the internet and start. Alteryx is way too expensive. In terms of pricing, it's always better to go with KNIME.

I am both consultant and a vendor right now. We do a bit of consultant work for some of our clients and we give the tutorials to them. We typically get in touch with them, and they send what they need and we do the distribution for them.

I'd recommend new users have their requirements sorted out first so that they know what they need out of the tool. If that is clear, they can install the custom content required in KNIME to get their analytics done correctly. If that is there, then it's a piece of cake.

Overall, I'd rate the solution eight out of ten. If the user interface was better and it offered better technical support, I would rate it higher.

I am just considering whether to use it or not. I am trying it to determine whether it is helpful or not. So far, it can solve my data analysis problems and I think it's a powerful data analysis tool.

It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop.

They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning.

The stability is great.

Most of the time it can solve the problems.

I have not used KNIME for a very long time so I have not used technical support so far.

Previously I used some programming tools, but I needed to do a lot of coding. KNIME is simpler to use.

The most important factor when I'm looking at which vendor or product to go with is the program's features.

I think the setup is straightforward.

I would rate it at nine out of 10. It's good, it makes thing easier.

Primarily used for advanced analytics, include designing and running predictive models, and conducting segmentation analysis. With KNIME, I connect to different data sources but usually need to conduct some data transformations before the main task is carried out. My results are usually written to a database, then I use a different tool for data visualization

It has allowed us to easily implement advanced analytics into various processes.

Easy to use nodes for ETL processes. This is because, in many cases, I usually transform the data before the main task even when the data is from a structured database.

Data visualization.

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. 

We are a solution provider and KNIME is a product that we are working on reselling to our customers. We sell BI tools such as Tableau and many of our customers that are using these tools need to have an AI solution. They have lots of use cases for AI including, for example, those from the financial sector would like to use AI for credit scoring. We also have government clients who will have their own specific use cases.

We have not yet sold it to any of our customers because they are still using the free tools and we are promoting it based on that.

The nicest part of KNIME is that the designer tool is free.

This solution is easy to use and it can be used to create any kind of model.

We are worried about the performance when it comes to using a lot of data that has many rows and columns. On the server-side, we are not sure whether KNIME can manage or handle large amounts of data without issue. It looks like it will easily work for small datasets but we are concerned about performance as the volume increases.

KNIME needs to provide more documentation and training materials, including webinars or online seminars. At this time, it is not sufficient when compared to some other vendors.

The user interface needs to be improved because it looks quite messy and I am not very comfortable using it. 

I have been familiar with KNIME for two or three years but we have been actively interested in it for less than a year.

We have not deployed the entire solution for a customer yet. However, we have been working with the design tool, which does not require deployment. You just have to download it and then it can be used for testing demonstration data.

The price of KNIME is quite reasonable and the designer tool can be used free of charge.

Many of our customers have streaming data and want to use an AI model. We do not yet know whether KNIME will handle live-streaming and it is something that we intend to test.

I would rate this solution a seven out of ten.

Providing the right solutions and consulting in revenue management requires rapid and comprehensive analysis in all areas. Such analysis makes it easier to look for patterns, where and when the cause of a problem is, especially when the solution has hundreds or more servers of different types and characteristics.

I use KNIME as a tool (ETL) in processing various logs and data (structured and unstructured format) then analyze and store the information in a database. This makes it easier to do the analysis and saves me time.

It very much helps me to do my job while supporting my organization's delivery of service to our client.

Most important, it is open-source. Next is the ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database.

I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports.

In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations.

I feel the query performance is slower than my old code. In my configurations, I set concurrence for a heavy query database, from multiple database sources, then transformed it before loading it into a destination database. It cannot do concurrent writing into databases if I use one database connection (user).

I’m not sure it is a lack in KNIME or in the database driver itself. To prevent the degredation of performance and system stability, I need to change the configuration of databases readers for output, write parameter onto the disk, not into memory.

I have never used tech support, but the community forums are quite good. Hopefully, there will be a knowledgebase, like VMware did.

I created my own script. I switched to KNIME because it simplifies the flow of my script into one workspace, and doesn't necessitate a lot of jobs in my system.

No, KNIME is my first choice because it's open-source and has features to combine with other scripts.

If you like data analysis, KNIME is the best option. It's free and easy to set up.