KNIME Reviews

Our primary use case for this solution is shopping-basket analysis.

It gives much insight about business in different aspects, from understanding product portfolio to clients, much of them arouse interests from important stakeholders

It is a complete data science platform and especially good at scaling up data preparation and wrapping. It provides a large numbers of algorithms to look at data from different angles

It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end.  The learning curve is steep.

We have been using this solution for six months.

Very good

Perfect

Very good and very prompt, usually in a few hours, can't complain more

I wrote Python in different IDE, however KNIME is easier to tackle with changes in data sources, processes, and expectations, etc. It is simple to work around with different situations for testing and comparison. 

Setup is very easy, no more than a normal app

I do it myself, and I am super-user level

More than worthy to invest

One of the benefit for KNIME is free for desktop version, which is enough for small team work. It is worthy to try but prepare for times to search and learn. It is not really as easy as drag and drop.

I check with Alteryx and RapidMiner

Official and structural online training is a must, which is now not enough. Or you can start something simple very easily but stuck to go deeper. However KNIME response is very prompt.

I am advocating the use of this solution in my organization. I use it personally for my purposes and for the company, I use it for internal data science with very good results.

What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.

It is difficult to say at this time, as I am not using the latest version. I have noticed that I don't have the latest modules that were added, such as ML.

In my opinion, there's one thing lacking. As far as algorithm notes go, it would be handy if category algorithms of C4 or C4.5 could be set with a checkbox or something like that.

Once you go to the forum or the documentation, to see how to implement a C4.5, you mark the checkbox, and only then it would be content for C4 or C4.5.

The documentation is lacking and it could be better. It's a community-driven product but there are a few crucial models missing such as ANOVA and MANOVA.

I have been using KNIME since December 2019.

In my opinion, it is very stable.

I have not yet explored the scalability as I am using it on my local machine and I don't have the experience of putting it on the cloud.

I do plan to increase my usage.

The documentation is okay, although there are things missing. At the same time, the forum support is great.

Previously, I was using SPSS Statistics, although that was ten years ago. I had a gap in data mining and the statistic field as a whole.

The initial setup is very straightforward.

It's an open-source solution.

I am considering further courses and maybe some certification in the next year.

I would strongly recommend KNIME. It's a modeling or statistics product that can be used by almost anyone with knowledge in the field. It works almost out of the box.

For starters, it's approximately two hours of watching videos and/or reading the documentation, and then off you go.

I built my first working model in two days when I started using KNIME, and it only needed to be tweaked. It was impressive.

I would rate this solution an eight out of ten.

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.

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.

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.

All analytics individuals use KNIME to integrate multiple sources of data (SQL, excel, etc.) and prep the data for static reporting.

We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics.

• Visual workflow creation
• Workflow variables (parameterisation)
• Automatic caching of all intermediate data sets in the workflow
• Scheduling with the server

The overall user experience feels unpolished. 

• Data field type conversion is a real hassle, and date fields are a hassle.
• Documentation is pretty poor.
• User community is average at best.

It is pretty stable.

Partially, only with very large datasets (10M+ records or so); its reliance on RAM is a bit high for normal PCs. Servers should be fine.

Not applicable (not local in South Africa).

Alteryx. KNIME is much cheaper. The KNIME desktop client is free. KNIME handles 95% of our requirements.

Straightforward.

KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required.

Alteryx.

I rate it a seven out of 10. It's very useful but needs polish and improved UX and UI in several areas.

For quick adoption, either get KNIME to provide training, or have a local knowledge expert on hand who is well versed with data workflow tools, and databases if necessary.

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 allows one to do many things, including data preparation. I was able to apply basic algorithms through just dragging and dropping. This in contrast to Python and other solutions, which involve much coding. 

I would prefer to have more connectivity. The user documentation is insufficient. I would like to see more enterprise level application. There are high end features which should appear, the MLOps platform being one. This feature is key. 

There should be better connectivity to such platforms as AWS and SageMaker, as we rely heavily on AWS in RL. For certain South Asian markets, we plan to go with Azure, so it is important to have connectivity to both of the major clouds.  There should be AI machine learning based algorithms. Such features should be available out of the box with good precision. 

I have worked with KNIME for a couple of months. 

The solution is stable.

KNIME assets are stand alone, as the solution is open source. I have not looked into their enterprise level application costs. While cost is a parameter, I would definitely consider other options which provide value for one's money. 

The solution is good for small scale implementation. Other solutions should be considered for enterprise level implementation. 

I rate KNIME as a five or six out of ten.