KNIME Reviews

We primarily use the solution as an ETL - to Extract, Transform, Load topics.

All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function.

Technical support has been extremely responsive so far.

The solution has a very strong and supportive community that shares information and helps each other troubleshoot.

The solution is very stable.

The initial setup is pretty simple and straightforward.

The user interface could be a little bit more comfortable. The usability, in general, could be much better.

The predefined workflows could use a bit of improvement.

I've been using the solution for five years.

The solution is pretty stable. I don't recall facing any bugs or glitches. The solution doesn't crash or freeze. It's fairly reliable overall.

We have about 10 users in the company that are on the solution regularly. They are all either engineers or business consultants.

The technical support on offer is very good. We rarely have issues, however, if we do, the support that is provided is very fast and the team is very knowledgeable. There's also a very strong and active KNIME community that includes KNIME developers.

At the end of the day, when we run into issues, it's dealt with immediately and we're very satisfied with the level of service provided.

The initial setup isn't complex. I'd describe it as straightforward. What makes it very easy is that there's so much material available online. Users have access to many forms of documentation, including webinars and training documents. When it comes to finer details, of course, it gets more complex, however, that's pretty normal in every product. 

We are KNIME partners and also provide KNIME training to clients as well.

We're a startup, and we're in the startup phase, however, we are working to build more business relationships with KNIME.

In my previous company, we were also partnered with KNIME. Currently, I'm working as a consultant and we have a customer who is using SAP Analytics, however, I am interested in learning more about the differences between KNIME and SAP Analytics. That's something I'm looking at right now.

We would definitely recommend this solution to other organizations.

I'd rate the solution eight out of ten. I don't know if there's a tool better than this particular product, however, there's always room for improvement on any technology.

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 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. 

I write weekly articles breaking down the previous week's Green Bay Packers' game. My main use for KNIME at this time is a workflow that takes play-by-play data from a CSV and puts it into a multi-tabbed Excel document, with all the stats I need for the week.

I had been doing this via a mix of Excel macros and some things by hand. Even with the macros, it would take me 30-plus minutes every week, and even that was just for the raw data to get to pivot tables. If I wanted additional calculations based on pivot table data, that would take even more time. With KNIME, I am able to get that process down to under one minute, with data broken down into individual tabs. It has changed my week.

I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data.

I'd like something that would make it easier to connect/parse websites, although I will fully admit that I'm not as proficient in KNIME as I would like to be, so it could be I'm just missing something.

No issues with stability.

No issues with scalability.

I haven't had to use technical support, but I have been able to find answers in the forum to any questions I have had.

I had been using a combination of Excel macros and manual entry. I switched because I was looking for something a bit quicker and automated, something to remove as much human error as possible.

I started simple, as I was learning the software as I went. It ended up being fairly complex. I still had some manual entry, but as I learned what KNIME was capable of, I kept building more and more to get everything as automated as possible.

I ran through a couple different options. None of them matched up to what KNIME could do.

Do training up front to make building workflows clean and easy from the start.

I've been using some of the analytics and photo manipulation features. I'm a teacher, and I've been using KNIME in my university.

It's a huge tool with machine learning features as well.

There are some parameters that I would like to have at a bigger scale. The upper limit of one node that tries to find spots or areas in photos was too small for us. It would need to be bigger.

I've been using this solution for two and a half years.

Overall. it's stable. However, every time there is a major version release, we end up having to upload many of the nodes again. It takes some time to do the version change.

It is scalable, especially with a server.

The technical support is excellent, fast, accurate, and friendly.

I used Power BI, but when I found KNIME, there was no going back.

The initial setup is easy.

You should do the KNIME courses, particularly the first two courses.
They would give you a very good base to make KNIME work nicely, and you would also know how it really works.

It is a really good system, and on a scale from one to ten, I would rate it at ten.

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. 

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.

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.