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.

All analytics individuals use KNIME to integrate multiple sources of data (SQL, excel, etc.) and prep the data for static reporting.

We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics.

• Visual workflow creation
• Workflow variables (parameterisation)
• Automatic caching of all intermediate data sets in the workflow
• Scheduling with the server

The overall user experience feels unpolished. 

• Data field type conversion is a real hassle, and date fields are a hassle.
• Documentation is pretty poor.
• User community is average at best.

It is pretty stable.

Partially, only with very large datasets (10M+ records or so); its reliance on RAM is a bit high for normal PCs. Servers should be fine.

Not applicable (not local in South Africa).

Alteryx. KNIME is much cheaper. The KNIME desktop client is free. KNIME handles 95% of our requirements.

Straightforward.

KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required.

Alteryx.

I rate it a seven out of 10. It's very useful but needs polish and improved UX and UI in several areas.

For quick adoption, either get KNIME to provide training, or have a local knowledge expert on hand who is well versed with data workflow tools, and databases if necessary.

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.

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.

I mainly used it to perform predictive modeling projects, such as customer-churn predictions and HR attrition predictions. The environments are mainly SQL-databases or CSV files.

The installation I worked with to perform the analyses was a regular laptop with no computational server behind it, which may have an impact on the capacity of the program handling very large databases or files.

The clients I performed the analyses for were all very pleased with the results. For churn prediction, one of the companies proactively started contacting clients with high risk to churn, resulting in drastically decreasing churn rates.

For organizations with a small team of data analysts or data scientists, it is a very easy tool to become familiar with predictive modeling, and makes it possible to hand over projects to colleagues without the need to extensively document them.

• The very easy-to-use visual interface
• Help functions and clear explanations of the functionalities and the used algorithms
• Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis

For inexperienced analysts or data scientists, it is a very easy tool to take your first steps in modeling and analytics.

The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R).

The program is not fit for handling very large files or databases (greater than  1GB); it gets too slow and has a tendency to crash easily.

I used it quite intensively for 10 months, long enough get familiar with it, to follow training, to use it in in several projects, to ask questions on the user forum.

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.

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.