KNIME Reviews

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.

Some of the projects that require KNIME are related to sales or the supply chain. We use it to aggregate data from diverse sources rather than predictive analytics. It's primarily for data collection, management, and preparation.

KNIME is fast and the visualization provides a lot of clarity. It clarifies your thinking because you can see what's going on with your data.

We find KNIME useful and convenient for data preparation.

KNIME's licensing and data management aren't as straightforward relative to Alteryx. Alteryx's tools are more sophisticated, so you need fewer to use it compared to KNIME. I think tab implementation could be easier, too.

My company has been using KNIME for about a year and a half. I don't work on the solution personally, but our analysts do. We use it off and on, depending on the project. It's not every day that we have projects requiring that, but we use it when we do.

KNIME is stable. We haven't seen any issues.

I think KNIME is scalable, but we haven't had a project that would test that sort of thing. The projects requiring KNIME aren't on a huge scale. We haven't tried to scale it, but I think it should be okay. 

My team has used the forum, but we never contacted support for any specific deployment query. I think the online documentation and training are quite sufficient.

We worked with Alteryx, but we switched to KNIME because that's what our customers wanted. Alteryx is a good solution, but the cost was exorbitant for the client. 

Setting up KNIME isn't hard. When we first installed the solution, it took one or two weeks to understand the various features. After doing it a few times, it didn't take as long. It only takes one or two people to deploy.

The ROI would depend on how KNIME is being used. Some projects would be more economically viable and save a lot of money. Others wouldn't save much.

KNIME could be cheaper, but it's okay compared to other solutions. Our projects are primarily within India, and it is extremely price-sensitive here. If we were doing projects in the UK or the US, it probably wouldn't matter.

I rate KNIME nine out of 10. I would recommend it. I think KNIME is an excellent solution.

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 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.

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.

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.

Providing the right solutions and consulting in revenue management requires rapid and comprehensive analysis in all areas. Such analysis makes it easier to look for patterns, where and when the cause of a problem is, especially when the solution has hundreds or more servers of different types and characteristics.

I use KNIME as a tool (ETL) in processing various logs and data (structured and unstructured format) then analyze and store the information in a database. This makes it easier to do the analysis and saves me time.

It very much helps me to do my job while supporting my organization's delivery of service to our client.

Most important, it is open-source. Next is the ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database.

I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports.

In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations.

I feel the query performance is slower than my old code. In my configurations, I set concurrence for a heavy query database, from multiple database sources, then transformed it before loading it into a destination database. It cannot do concurrent writing into databases if I use one database connection (user).

I’m not sure it is a lack in KNIME or in the database driver itself. To prevent the degredation of performance and system stability, I need to change the configuration of databases readers for output, write parameter onto the disk, not into memory.

I have never used tech support, but the community forums are quite good. Hopefully, there will be a knowledgebase, like VMware did.

I created my own script. I switched to KNIME because it simplifies the flow of my script into one workspace, and doesn't necessitate a lot of jobs in my system.

No, KNIME is my first choice because it's open-source and has features to combine with other scripts.

If you like data analysis, KNIME is the best option. It's free and easy to set up.