We are users of KNIME and we also resell this product.
The primary use case is for data analysis.
We are users of KNIME and we also resell this product.
The primary use case is for data analysis.
KNIME has a good set of features for data analytics.
The sampling features are some of the most important ones for us.
Compared to the other data tools on the market, the user interface can be improved.
There are quite a few features that are not available in the Community Edition. Having more of these features brought into the platform would be helpful.
Support from KNIME could be enhanced, although the community support is great.
I have been working with KNIME for between three and four years.
It has been stable for a while, since the release of version 4. However, between version 2 and version 3, it was quite unstable. The stability has improved.
KNIME is quite scalable, which is one of the most important features that we found.
The support for KNIME is good, although it comes from the community rather than from KNIME itself.
The initial setup is quite simple, especially if you have already worked on other tools.
The deployment can be completed within a couple of hours, including the setup of permissions and other configurations.
There is a Community Edition and paid versions available.
For anybody who is looking for a new data science platform, KNIME is a product that I can recommend.
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.
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 primarily use this product for data engineering and data wrangling.
The most valuable feature is the data wrangling, which is what I mainly use it for.
From the point of view of the interface, they can do a little bit better.
I have been using KNIME for three years.
Scalability is not a relevant consideration for KNIME because I am using it myself.
I feel that the community is a bit too Java-oriented. It would be better if it grew and became more diversified, from a data engineering perspective.
The initial setup is straightforward.
There are different licenses available.
The only other option I have is Alteryx and the functions in KNIME are better.
This is a very handy tool and I use it quite interactively. I am not an expert-level user and it pretty much has everything that I need.
I would rate this solution an eight out of ten.
I am a basic user, doing a data science course.
I am using Knime more from a study perspective, rather than a practical work application.
I am fairly competent with creating workflows and automating some basic things in Knime.
NA
From a user-friendliness perspective, it's a great tool.
I think some of the online training content could be better, although I have been able to find all of the information. At times they're quite lengthy, and for me to go through everything and then get a resolution takes a good amount of time.
They could have a more structured node-wise training model, where I can simply get into it. For example, if I need to understand a node to create pivot tables, I have to go through the training mode to understand what the functionality is.
If they had a more structured training model it would be very helpful.
It would be helpful to launch more certification programs online.
There could be better marketing. The awareness of Knime is limited, especially for small organizations. When you compare with PowerBI, there is a lot of active marketing put into their product, also, having Microsoft associated with it is an advantage.
They have to step up on the marketing aspect and the ability to digitize using Knime. Many are aware of other tools such as PowerBI.
In the next release, I would like to see the certification available for active users.
Also the costing aspect of the certification, there could be more local impact time zone programs with a bit of costing dissension to encourage more active users in Knime who can then move to the server version in their organization.
I have been using Knime for approximately one year.
We are in the testing phase of this solution.
It is quite stable. I have not been faced with any issues since I have been using this solution.
Knime is a scalable product.
We have five power users who are test prototype users at the moment. We are trying to sell that prototype to the management so that we can deploy it on a larger scale.
I have never had the need to reach out to technical support.
At some point when I move on with the server versions, I might need some help.
The desktop version, it's more of an install and then just run it.
At this time, I am using the free version of Knime.
I did look at Tableau and was considering it to some extent. I felt Knime was more user-friendly and more versatile to automate tasks (most of them excel based).
I am quite supportive of this product. It has been helpful in automating a few of my accounting activities.
Digital groups such as Knime have great potential, but there needs to be more aggressiveness with marketing. There are many executives that do not know what Knime is.
My journey starts by explaining what Knime is and what the functionalities are.
I like Knime, and I would rate this solution a nine out of ten.
Our analysts use Knime in the company for data modeling, data wrangling, and data preparation. We have a good amount of data that we work with.
I do not personally use the product, but I am familiar with its usage through my analysts.
Data preparation and data modeling are easy to do.
It is very fast to develop solutions.
There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool. This would make workflow development faster because several tools could be used together, based on the function that is chosen. Each would complete one of the constituents of the task.
We have been working with Knime for approximately one year.
This product is quite stable and we haven't had any problems.
Knime is a scalable solution and we haven't experienced any issues. There are six of us who are using it.
Prior to Knime, we were using Alteryx. However, Alteryx is too costly and our customers don't want to pay for it.
The initial setup is easy.
The price for Knime is okay.
I would rate this solution a nine out of ten.
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.
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.

I am currently trying out the platform for evaluation purposes from a Business Analyst perspective as I am not a Data Science specialist. Up to now I have found it to be quite an intuitive platform to gain a better insight into the impact that data science has in solving real-life problems today.
The main use for us is to gain a better understanding of how the technology can be utilised from a layperson's perspective to tackle real-life business issues.