KNIME Reviews

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.

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.

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 use KNIME for clustering data analysis. 

It's a coding-less opportunity to use AI. This is the major value for me.

I had some difficulty connecting to servers. It asked me to set something up on my server and it asked me for a code that I needed to generate on the server. There were several steps that I messed up. I followed all of the instructions but I couldn't manage it at all. I followed the directions in several forums to find out the problem.  

There should be better documentation and the steps should be easier. 

I have been using KNIME for three to four months. 

I haven't needed to contact their support. 

I tried Python and Microsoft. 

The initial setup was super easy. It was really quick. I did it myself for personal use. It didn't take longer than half an hour. 

Some of the samples are outdated but my advice to someone considering KNIME is to use their samples. 

I would rate KNIME an eight out of ten. 

In the next release, the should have more comprehensive samples.

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.

We are using KNIME for basic analytics to reduce the amount of processing time. We found that it takes a lot of time for scripting on the cloud, so we have been using it locally on our PCs.  

While the product has not yet improved our organization, we expect to use it in full deployments with our clients to greatly reduce their costs and make our services more attractive.  

The most valuable part of the solution is the machine learning part. The second feature that we use most is big data connectivity. When we deployed the architecture, we directed our IDS (Intrusion Detection System) server to where the big data will be on our servers. Then we needed some kind of basic machine learning and obviously. After that, we connected it with Tableau visualization. Now we are writing the big data part of our solution along with the overall machine learning. These two parts will be the most important for our business going forward.  

I think also connectivity with hybrid databases and also integration with languages like Python are great advantages to what we are seeking to do in our environment. We have been using these features extensively and we find them to be very valuable in achieving what we hoped to achieve with the tool.  

One thing that I found was that in the open-source version of the KNIME analytics platform, we see difficulties in scheduling jobs. If the scheduler could be updated in the open-source version, the software will be easier to schedule properly and to use efficiently.  

The second time that I faced difficulty using KNIME was with data processing time. When we use large chunks of data for local processing, the processing is very slow. We do not want to move these big data often. For me, it seemed that moving one gigabyte of data went very slowly. So, the second thing that I would really like to see is a better ability to handle large amounts of data locally with KNIME in an efficient manner.  

The third area that might be improved is that when we have a large amount of data — let's say like five gigabytes — then there is one panel completely ignored. The impact of that on the results of our data processing is not good. So I would really like to see the load balancing and the overall processing time substantially reduced.  

So the things I would most like to see are the ability to handle large amounts of data and improved performance in processing.  

We have been working with KNIME for about six months.  

We do not have many people using the solution in our company at this point because the tool is comparatively new to us. There are around three or four users right now. We do have plans to increase the usage and the number of users. We have been planning it because we have growth opportunities with some clients. The only potential problem is that right now, we are under-confident, in our capability to implement pure KNIME solutions without more discovery and testing. So, we are planning it to replace Alteryx eventually with KNIME. But as of now, we are just planning. We do plan to increase the usage in the future but we have not done anything yet regarding that.  

The initial setup was very straightforward. It was not complex at all.  

We deployed it, we installed it ourselves on our local system server.  

We have done a few projects with some of our clients in KNIME. In these cases, we mainly used KNIME because of its ability to work in a data center environment in an enterprise system. This was one of the most important things that we were looking for. The second point was that KNIME is an open-source analytics platform. The point is that if some client has less data or a relatively small database, then we can use the open-source platform instead of using Alteryx, which is fairly expensive. These are the options we advise our clients about.  

On a scale from one to ten where one is the worst and ten is the best, I would rate this product as an eight out of ten. I honestly do not feel familiar enough with this product that my rating is accurate as I need to be more familiar with it over time. On the other hand, I have used KNIME and other tools in a similar category — like Informatica and Alteryx. Informatica is purely a data warehouse software. Alteryx is something we use frequently. So I have used three ETL tools. If I compared KNIME with Alteryx which are the most similar of the three, then I think KNIME is much better for our purposes. Strictly as a comparison with Alteryx, I would rate KNIME as an eight.