KNIME vs RapidMiner vs SAS Enterprise Miner comparison

Read 22 RapidMiner reviews

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4,232 Comparison Views

95% willing to recommend

SAS Enterprise Miner

Read 13 SAS Enterprise Miner reviews

669 Views
543 Comparison Views

93% willing to recommend

KNIME

SAS Enterprise Miner

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between KNIME, RapidMiner, and SAS Enterprise Miner based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Knime, Amazon Web Services (AWS) and others in Data Science Platforms.

To learn more, read our detailed Data Science Platforms Report (Updated: January 2025).

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of KNIME is 11.3%, up from 9.4% compared to the previous year. The mindshare of RapidMiner is 7.7%, up from 5.9% compared to the previous year. The mindshare of SAS Enterprise Miner is 0.7%, down from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Shyam_Sridhar

IT Specialist at a computer software company with 51-200 employees

Good for data analysis to model prediction and application but data load limitations

KNIME is very easy to handle and use. Anyone can use it, and it's easy to learn. You don't need a specific class. They're very good at model prediction. It has got everything. From data analysis to model prediction and application, it's very good. I only use the free community edition, not the enterprise one. I feel KNIME is really good. I haven't tried any other tool or platform yet, but KNIME is pretty good. The workflow is great. You drag and drop, and then you have the data explorer and charts that give results. The execution is also good – it's easy to identify where your model has gone wrong. It shows you the exact point of error within the workflow, so you don't have to execute the entire workflow to find it. For example, if your workflow has ten steps and the error is in the sixth step, it will show you the error at that step. You don't have to worry about the first five steps. The Data Explorer is very good, and the charts are great too. The accuracy charts for different models, like decision tree, K3, Naive Bayes, are all very good. KNIME is great at reporting, whether it's structured or unstructured data. These are all very good features.

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Rathnam Makam

Senior Technical Manager at a computer software company with 1,001-5,000 employees

A no-code tool that helps to build machine learning models

One challenge I encountered while implementing RapidMiner was the lack of documentation. Since there aren't as many users, finding resources to learn the tool was initially difficult. To overcome this hurdle, I believe RapidMiner could improve by providing more tutorials tailored for new users. I haven't explored the tool's latest version, so I'm unaware of the current features. However, I think it would be beneficial if they could enhance capabilities related to deep neural networks, provide better support for generating UI, and allow for importing and utilizing large language models.

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reviewer1447110

Senior Systems Engineer at a financial services firm with 10,001+ employees

Good technical support but too complex and not open-source

We're using Enterprise Guide simultaneously with Enterprise Miner. From my perspective, I believe that open-source analytics tools are closer to fitting our needs. We prefer open-source options like Anaconda. They offer good support and features. Anaconda also integrates well with Jupyter NET, which is important for us. Overall, on a scale from one to ten, I'd rate the solution at a five. If there were better protocols and wasn't as complex as it is, I'd rate it a bit higher.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The product is open-source and therefore free to use."

"It is very fast to develop solutions."

"Since KNIME is a no-code platform, it is easy to work with."

"It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured."

"I was able to apply basic algorithms through just dragging and dropping."

"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."

"KNIME is more intuitive and easier to use, which is the principal advantage."

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

More KNIME pros

"RapidMiner is a no-code machine learning tool. I can install it on my local machine and work with smaller datasets. It can also connect to databases, allowing me to build models directly on the data stored there. RapidMiner offers a wider range of operators than other tools like Dataiku, making it a better option for my needs."

"The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."

"The most valuable feature is what the product sets out to do, which is extracting information and data."

"RapidMiner for Windows is an excellent graphical tool for data science."

"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."

"The solution is stable."

"The documentation for this solution is very good, where each operator is explained with how to use it."

"Using the GUI, I can have models and algorithms drag and drop nodes."

More RapidMiner pros

"I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks."

"The technical support is very good."

"I like the way the product visually shows the data pipeline."

"he solution is scalable."

"The solution is very good for data mining or any mining issues."

"The most valuable feature is the decision tree creation."

"Good data management and analytics."

"The solution is able to handle quite large amounts of data beautifully."

More SAS Enterprise Miner pros

Cons

"The solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon."

"KNIME's documentation is not strong."

"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."

"KNIME can improve by adding more automation tools in the query, similar to UiPath or Blue Prism. It would make the data collection and cleanup duties more versatile."

"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."

"To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages."

"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."

"I wish there were more video training resources for KNIME. The current videos are very short, and most learning is text-based. Longer training sessions would be helpful, especially for complex flowchart use cases. Webinars focusing on starting projects and analyzing data would also be beneficial."

More KNIME cons

"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."

"I think that they should make deep learning models easier."

"RapidMiner can improve deep learning by enhancing the features."

"The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team. If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery. However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator."

"The price of this solution should be improved."

"The server product has been getting updated and continues to be better each release. When I started using RapidMiner, it was solid but not easy to set up and upgrade."

"The visual interface could use something like the-drag-and-drop features which other products already support. Some additional features can make RapidMiner a better tool and maybe more competitive."

"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."

More RapidMiner cons

"The user interface of the solution needs improvement. It needs to be more visual."

"The visualization of the models is not very attractive, so the graphics should be improved."

"Technical support could be improved."

"The product must provide better integration with cloud-native technologies."

"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."

"Virtualization could be much better."

"The initial setup is challenging if doing it for the first time."

"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."

More SAS Enterprise Miner cons

Pricing and Cost Advice

"KNIME is a cheap product. I currently use KNIME's open-source version."

"While there are certain limitations in functionality, you can still utilize it efficiently free of charge."

"KNIME assets are stand alone, as the solution is open source."

"For beginners, the free desktop version is very attractive, but the full server version can be more expensive. I have only used the free version and it offers a fair pricing system. I have been promoting it to others without any compensation or request from the company, simply because I am enthusiastic about it. I am not aware of the pricing for the server version, but it seems to be widely used."

"At this time, I am using the free version of Knime."

"KNIME is free and open source."

"With KNIME, you can use the desktop version free of charge as much as you like. I've yet to hit its limits. If I did, I'd have to go to the server version, and for that you have to pay. Fortunately, I don't have to at the moment."

"This is a free open-source solution."

More KNIME pricing and cost advice

"I used an educational license for this solution, which is available free of charge."

"I'm not fully aware of RapidMiner's price because we had licenses provided, but from my analysis, it's moderately priced, not too high or too low. It's worth the investment."

"For the university, the cost of the solution is free for the students and teachers."

"Although we don't pay licensing fees because it is being used within the university, my understanding is that the cost is between $5,000 and $10,000 USD per year."

"The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license."

"This solution is for large corporations because not everybody can afford it."

"The solution must improve its licensing models."

"The solution is expensive for an individual, but for an enterprise/institution (purchasing bulk licenses), it is not a high price for the use that will come from it."

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Manufacturing Company

12%

Computer Software Company

Educational Organization

University

12%

Computer Software Company

11%

Financial Services Firm

10%

Educational Organization

10%

Financial Services Firm

25%

University

13%

Insurance Company

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about KNIME?

Since KNIME is a no-code platform, it is easy to work with.

What is your experience regarding pricing and costs for KNIME?

I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.

What needs improvement with KNIME?

For graphics, the interface is a little confusing. So, this is a point that could be improved.

What do you like most about RapidMiner?

RapidMiner is a no-code machine learning tool. I can install it on my local machine and work with smaller datasets. I...

What is your experience regarding pricing and costs for RapidMiner?

I'm not fully aware of RapidMiner's price because we had licenses provided, but from my analysis, it's moderately pri...

What needs improvement with RapidMiner?

The product must provide data-cleaning features. I could not use RapidMiner for data cleaning in one of my projects a...

What do you like most about SAS Enterprise Miner?

I like the way the product visually shows the data pipeline.

What is your experience regarding pricing and costs for SAS Enterprise Miner?

The solution must improve its licensing models. It bundles all the products into smaller products. We can only have a...

What needs improvement with SAS Enterprise Miner?

The product must provide better integration with cloud-native technologies.

Microsoft Power BI vs KNIME

Comparisons

Compared 16% of the time

Dataiku vs KNIME

Compared 13% of the time

Alteryx vs KNIME

Compared 10% of the time

Weka vs KNIME

Compared 7% of the time

Microsoft Azure Machine Learning Studio vs KNIME

Compared 6% of the time

More KNIME Competitors

Dataiku vs RapidMiner

Compared 16% of the time

Alteryx vs RapidMiner

Compared 9% of the time

Tableau vs RapidMiner

Compared 6% of the time

Microsoft Azure Machine Learning Studio vs RapidMiner

Compared 4% of the time

TIBCO Statistica vs RapidMiner

Compared 1% of the time

More RapidMiner Competitors

SAS Visual Analytics vs SAS Enterprise Miner

Compared 39% of the time

IBM SPSS Modeler vs SAS Enterprise Miner

Compared 23% of the time

Microsoft Azure Machine Learning Studio vs SAS Enterprise Miner

Compared 11% of the time

More SAS Enterprise Miner Competitors

Product Reports

KNIME

Download KNIME product report

Download RapidMiner product report

Download SAS Enterprise Miner product report

Data Mining

December 2024

Also Known As

KNIME Analytics Platform

No data available

Enterprise Miner

Overview

KNIME is an open-source analytics software used for creating data science that is built on a GUI based workflow, eliminating the need to know code. The solution has an inherent modular workflow approach that documents and stores the analysis process in the same order it was conceived and implemented, while ensuring that intermediate results are always available.

KNIME supports Windows, Linux, and Mac operating systems and is suitable for enterprises of all different sizes. With KNIME, you can perform functions ranging from basic I/O to data manipulations, transformations and data mining. It consolidates all the functions of the entire process into a single workflow. The solution covers all main data wrangling and machine learning techniques, and is based on visual programming.

KNIME Features

KNIME has many valuable key features. Some of the most useful ones include:

Scalability through data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
High extensibility via a well-defined API for plugin extensions
Intuitive user interface
Import/export of workflows
Parallel execution on multi-core systems
Command line version for "headless" batch executions
Activity dashboard
Reporting & statistics
Third-party integrations
Workflow management
Local automation
Metanode linking
Tool blending
Big Data extensions

KNIME Benefits

There are many benefits to implementing KNIME. Some of the biggest advantages the solution offers include:

Integrated Deployment: KNIME’s integrated deployment moves both the selected model, and the entire data model preparation process into production simply and automatically, allowing for continuous optimization in production and also saving time because it eliminates error.

Elastic and Hybrid Execution: KNIME’s elastic and hybrid executions helps you reduce costs while covering periods of high demand, dynamically.

Metadata Mapping: KNIME enables complete metadata mapping of all aspects of your workflow. In addition, KNIME offers blueprint workflows for documenting the nodes, data sources, and libraries used, as well as runtime information.

Guided Analytics: KNIME’s guided analytics applications can be customized based on reusable components.

Powerful analytics, local automation, and workflow difference: KNIME uses advanced predictive and machine learning algorithms to provide you with the analytics you need. In combination with powerful analytics, KNIME’s automation capabilities and workflow difference prepare your organization with the tools you need to make better business decisions.

Supports enterprise-wide data science practices: The deployment and management functionalities of KNIME make it easy to productionize data science applications and services, and deliver usable, reliable, and reproducible insights for the business.

Helps you leverage insights gained from your data: Using KNIME ensures the data science process immediately reflects changing requirements or new insights.

Reviews from Real Users

Below are some reviews and helpful feedback written by PeerSpot users currently using the KNIME solution.

An Emeritus Professor at a university says, “It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional. It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.”

Benedikt S., CEO at SMH - Schwaiger Management Holding GmbH, explains, “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.”

Piotr Ś., Test Engineer at ProData Consult, says, “What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.”

Knime

RapidMiner's unified data science platform accelerates the building of complete analytical workflows - from data prep to machine learning to model validation to deployment - in a single environment, improving efficiency and shortening the time to value for data science projects.

SAS Enterprise Miner is a solution to create accurate predictive and descriptive models on large volumes of data across different sources in the organization. SAS Enterprise Miner offers many features and functionalities for the business analysts to model their data. Some of the business applications are for detecting fraud, minimizing risk, resource demands, reducing asset downtime, campaigns and reduce customer attrition.

SAS

Sample Customers

Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare

Data Science Platforms