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KNIME is the #1 ranked solution in our list of top Data Mining tools. It is most often compared to Alteryx: KNIME vs Alteryx

What is KNIME?
KNIME is the leading open platform for data-driven innovation helping organizations to stay ahead of change. Use our open-source, enterprise-grade analytics platform to discover the potential hidden in your data, mine for fresh insights or predict new futures.

KNIME is also known as KNIME Analytics Platform.

KNIME Buyer's Guide

Download the KNIME Buyer's Guide including reviews and more. Updated: October 2021

KNIME Customers
Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
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Archived KNIME Reviews (more than two years old)

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LL
Intern at a energy/utilities company with 10,001+ employees
Real User
Fast problem solving with minimal coding, I just drag and drop

Pros and Cons

  • "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."

What is our primary use case?

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.

What is most valuable?

It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop.

What needs improvement?

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.

For how long have I used the solution?

Trial/evaluations only.

What do I think about the stability of the solution?

The stability is great.

What do I think about the scalability of the solution?

Most of the time it can solve the problems.

How are customer service and technical support?

I have not used KNIME for a very long time so I have not used technical support so far.

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

I think the setup is straightforward.

What other advice do I have?

I would rate it at nine out of 10. It's good, it makes thing easier.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
it_user837522
Data Science Consultant
Consultant
Very easy-to-use visual interface; Data Wrangling and looping help automate analysis

Pros and Cons

  • "Key features include: 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."
  • "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."

What is our primary use case?

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.

How has it helped my organization?

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.

What is most valuable?

  • 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.

What needs improvement?

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.

For how long have I used the solution?

Less than one year.

What other advice do I have?

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.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Learn what your peers think about KNIME. Get advice and tips from experienced pros sharing their opinions. Updated: October 2021.
541,462 professionals have used our research since 2012.
GM
Senior Data Scientist
Real User
Clear view of the data at every step of ETL process enables changing the flow as needed

Pros and Cons

  • "Clear view of the data at every step of ETL process enables changing the flow as needed."
  • "We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders."
  • "The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine."
  • "Easy to connect with every database: We use queries from SQL, Redshift, Oracle."
  • "​The data visualization part is the area most in need of improvement."

What is our primary use case?

We use KNIME for two main reasons:

Automation: The main purpose of our utilization it to run scheduled workflows (such as CRM campaigns, business reports, etc.) on a recurrent basis. We have created ETLs to automate most of the recurrent tasks and we let it run via batch files.

Ad-hoc business cases: We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders.

How has it helped my organization?

In addition to leveraging KNIME flexibility to query data from our database for ad-hoc business cases, the product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine. In some particular cases, my team builds the workflow and then we hand it over to the stakeholder, who can run it on his own by inserting a variable or changing a few settings in the workflows.

What is most valuable?

  • Easy to connect with every database: We use queries from SQL, Redshift, Oracle.
  • Easy to have a clear view of the data at every single step of the ETL process, with the consequent possibility of changing the flow according to your needs.

What needs improvement?

I think the data visualization part is the area most in need of improvement.

For how long have I used the solution?

Three to five years.

What other advice do I have?

I’ve been using KNIME for more than four years now. I started using it in the company I was working for in Rome (Paddy Power Italy), then I moved to headquarters in Dublin (Paddy Power Ireland/UK) and started using it for their business. Eventually, I moved to the United States and started using it for my current company (TVG-Betfair) and it is currently the main analytics tool in both our offices (New Jersey and Los Angeles).

I would definitely rate it a nine out of 10. I am very happy with the product and it would be hard to find something better in the market.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
it_user837516
Business Analyst at a retailer with 501-1,000 employees
Real User
Allows me to integrate several data sets quickly and easily, to support analytics

Pros and Cons

  • "We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics."
  • "Valuable features include 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. In particular: Data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best."

What is our primary use case?

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

How has it helped my organization?

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

What is most valuable?

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

What needs improvement?

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.

For how long have I used the solution?

Less than one year.

What do I think about the stability of the solution?

It is pretty stable.

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

Not applicable (not local in South Africa).

Which solution did I use previously and why did I switch?

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

How was the initial setup?

Straightforward.

What's my experience with pricing, setup cost, and licensing?

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

Which other solutions did I evaluate?

Alteryx.

What other advice do I have?

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.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Evans Otalor
Business Intelligence Manager at Telecoms
Real User
It has allowed us to easily implement advanced analytics into various processes

What is our primary use case?

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

How has it helped my organization?

It has allowed us to easily implement advanced analytics into various processes.

What is most valuable?

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.

What needs improvement?

Data visualization.

For how long have I used the solution?

What is our primary use case?

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

How has it helped my organization?

It has allowed us to easily implement advanced analytics into various processes.

What is most valuable?

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.

What needs improvement?

Data visualization.

For how long have I used the solution?

One to three years.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Dusty Evely
Business Analyst at a tech services company with 201-500 employees
Real User
Rule Engine allows me to create lookup tables on the fly

Pros and Cons

  • "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."

What is our primary use case?

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.

How has it helped my organization?

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.

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

One to three years.

What do I think about the stability of the solution?

No issues with stability.

What do I think about the scalability of the solution?

No issues with scalability.

How are customer service and technical support?

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.

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

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.

Which other solutions did I evaluate?

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

What other advice do I have?

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

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Agus Kurdiyanto
Solution Integrator at a comms service provider with 11-50 employees
Real User
Helps me collect, reformat, load data from multiple sources into one db, but needs visualization features

Pros and Cons

  • "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."

What is our primary use case?

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.

How has it helped my organization?

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

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

One to three years.

What do I think about the stability of the solution?

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.

How are customer service and technical support?

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

Which solution did I use previously and why did I switch?

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.

Which other solutions did I evaluate?

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

What other advice do I have?

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

Disclosure: I am a real user, and this review is based on my own experience and opinions.
PD
Data Scientist at a tech services company with 1,001-5,000 employees
Real User
We have been able to appreciate the considerable reduction in prototyping time

What is our primary use case?

We have used KNIME for text processing, specifically for leveraging the text processing features for entity extraction, document classification, relationship extraction, and other such NLP tasks.

How has it helped my organization?

We are far from reaping the benefits of this platform as an organization. However, so far, we have been able to appreciate the considerable reduction in prototyping time.

What is most valuable?

The most useful features are the readily available extensions that speed up the work. For instance, KNIME offers multiple document taggers, which one can use with relative ease. Similarly, the number of predefined NER taggers are also very handy.

What needs improvement?

The documentation needs a proper rework. 

For how long

What is our primary use case?

We have used KNIME for text processing, specifically for leveraging the text processing features for entity extraction, document classification, relationship extraction, and other such NLP tasks.

How has it helped my organization?

We are far from reaping the benefits of this platform as an organization. However, so far, we have been able to appreciate the considerable reduction in prototyping time.

What is most valuable?

The most useful features are the readily available extensions that speed up the work. For instance, KNIME offers multiple document taggers, which one can use with relative ease. Similarly, the number of predefined NER taggers are also very handy.

What needs improvement?

The documentation needs a proper rework. 

For how long have I used the solution?

Less than one year.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
it_user461148
Partner, Turkey and The Netherlands at a tech vendor with 201-500 employees
Real User
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.

What is our primary use case?

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.

How has it helped my organization?

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.

What is most valuable?

  • 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

What needs improvement?

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.

For how long have I used the solution?

One to three years.

What do I think about the stability of the solution?

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. 

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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.

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

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.

What about the implementation team?

We did in-house.

What was our ROI?

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.

What's my experience with pricing, setup cost, and licensing?

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.

Which other solutions did I evaluate?

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.

What other advice do I have?

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.

Disclosure: My company has a business relationship with this vendor other than being a customer: Partnership with KNIME
Dharma Teja
Associate Analyst at a consultancy with 1,001-5,000 employees
Consultant
Easy to setup, it organises workflows in very neat manner

Pros and Cons

  • "Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing."
  • "System resource usage. Knime will occupy total system RAM size and other applications will hang."

What is most valuable?

Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing.

How has it helped my organization?

Lot of manual work has been automated through this and single solution created on this can be used by others, too. We have implemented the same thing in our organization as CAE.

What needs improvement?

Data handling capacity. Currently it handless 25M rows of data, after which you will face lagging issue.

System resource usage. Knime will occupy total system RAM size and other applications will hang.

For how long have I used the solution?

I have been using it for 18 months.

What do I think about the stability of the solution?

Nope.

What do I think about the scalability of the solution?

When the data limit exceeds, it will get lag.

How are customer service and technical support?

A nine out of 10.

Which solution did I use previously and why did I switch?

Previously, we used Alteryx for harmonizing. Cost was the main reason for the switch.

How was the initial setup?

It is easy to setup.

What's my experience with pricing, setup cost, and licensing?

It is free of cost. It is GNU licensed.

Which other solutions did I evaluate?

No.

What other advice do I have?

It is best one for harmonizing data with no cost included.

Disclosure: My company has a business relationship with this vendor other than being a customer: We are partners of the KNIME tool.
ITCS user
CEO with 11-50 employees
Vendor
The diversity of native algorithms could be improved but it is very easy to use​

What is most valuable?

  • It has an excellent interface
  • Is very easy to use
  • It really boosts productivity
  • The performance is impressive, even in a normal laptop
  • The diversity of tools for pre-processing is very handy
  • The integration with R and Weka, make it the perfect Data Mining solution

How has it helped my organization?

We have use it in industry projects, for internal experiments, and for teaching.

What needs improvement?

The diversity of native algorithms could be improved.

For how long have I used the solution?

I have been using this solution for the past three years and it is an excellent solution.

What was my experience with deployment of the solution?

No issues with deployment.

What do I think about the stability of the solution?

No issues with stability.

What do I think about the scalability of the solution?

No issues with scalability.

How are customer service and technical support?

Customer Service:

Once I had to contact the customer service, and it worked quite well.

Technical Support:

Knime has a considerable support community, so there are few problems that you can not find in the documents of the community.

Which solution did I use previously and why did I switch?

I used the old Clementine solution (now in the IBM portfolio). I also keep using Weka, but now I tend to use Knime more, which ends up being more versatile, since it can integrate R and Weka.

How was the initial setup?

The setup is straightforward, just download and start using.

What about the implementation team?

I implemented in-house and for other companies.

What was our ROI?

Very high, since it has low costs and it is very easy to use.

Which other solutions did I evaluate?

Yes, but long ago. I evaluated Oracle Data Mining, Clementine, and SAS Enterprise Miner.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
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