KNIME Reviews

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.

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.  

In demand forecasting projects to extract, to clean and to transform data from various resources. Also some clustering and classification techniques are used for behavioural clustering and classification according to attributes.

My organization's field of activity is to develop business applications for niche areas. Almost three years ago, we decided to extend our solutions with advanced analytics. KNIME let us start easy and fast into the Advanced Analytics area. We are able to try project ideas with KNIME by doing proof of concept easy and prototyping fast.

• Easy ETL operations
• Rich algorithm set
• Integrated with other languages like R, Python, and Java.
• Works together with other technologies like DeepLearning4j, H2O.ai, D3.js, and Weka.
• Ease of use and being a performant solution.
• Continues development and wide community support

I mentioned about the distributed architecture in my previous answer, but they did with version 3.5. This time maybe I could add the integration with graph databases like Neo4j.

In the previous versions, I had some issues when reading large Excel files due to memory usage. But with the previous version (3.3), they renewed all Excel nodes and now I have no problem. 

With the data sizes that I dealt with, I did not. Our average record size was around 10 million records. If we have bigger data, we can opt for a Big Data extension for Hadoop, Spark, etc.

I used it very little. All of them replied to me in one day. (It was not professional support, just over a forum). Also, I can find enough information in the documentation and forum.

Before KNIME, I used SQL language and Excel for data analysis but machine learning algorithms. In parallel to KNIME, I worked on a few projects with R and Python separately. So I cannot say that I switched from different solutions.

But just for ETL with Excel, KNIME brings me better visualization, rich function set, preserving operations to repeat again and better performance on the same hardware.

I am using Mac and it is so easy. Download a .dmg, extract it as an app, and copy it to the applications folder. On windows it is also simple installation.

For extensions like R or Python, you need experience with general OS and installation processes.

We did in-house.

The biggest ROI comes from productivity when creating new things and also supporting old jobs.

And there is no hidden cost. Licensing is simple and open than other platforms.

KNIME is open sourced platform and has a free desktop version with unlimited data size and functionality.

Also, the server version is good for teams and enterprise productivity. Especially the new "Model Factory", which lets data science teams easily build and manage models. When compared with similar products, it is less expensive but as powerful as (or maybe more powerful than) others.

The Open Source licensing and community support is one of our important criteria. The second one is the interoperability with other technologies and openness to different data sources. There are two options: RapidMiner and KNIME. We chose KNIME.

Data Science requires freedom for creativity. Sometimes you need to crawl data from the web or social media. Sometimes you need to blend different sources like NoSQL MongoDB and Excel files, etc. It is not only algorithms and data extraction, visualization and preparation steps are important as at least algorithms.

Don't go with software that has complex and hidden licensing costs, which will kill your flexibilty and creativity. Also, interoperability brings the advantage of limitlessness.

We use this solution primarily for automation processing, source data, and for data lake or databases. 

I like the visual features and that we can connect using a pixel or an icon for the sources and controls. KNIME is easy to use, it's stable, powerful, and has a lot of features.

For now, the license is quite expensive for us and it would be helpful if that was reduced.

I've been using this solution for a few years. 

The solution is stable. 

We have five people using this solution so we haven't yet tested the scalability. 

We use the community for any questions we have. 

I think the deployment requires a specialist but once it's implemented it doesn't need any maintenance. 

We use the community version; the enterprise version is expensive for us. If we need better support or have other requirements we might need to move to the enterprise version. 

I rate this product nine out of 10.