Altair RapidMiner vs Dataiku vs KNIME comparison

Read 12 Dataiku reviews

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5,579 Comparison Views

100% willing to recommend

KNIME

Read 60 KNIME reviews

8,124 Views
5,819 Comparison Views

94% willing to recommend

Altair RapidMiner

KNIME

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Altair RapidMiner, Dataiku, and KNIME 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: May 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 May 2025, in the Data Science Platforms category, the mindshare of Altair RapidMiner is 7.9%, up from 6.8% compared to the previous year. The mindshare of Dataiku is 12.8%, up from 8.4% compared to the previous year. The mindshare of KNIME is 11.9%, up from 9.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Laurence Moseley

Emeritus Professor of Health Services Research at University of South Wales

Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms

When I started using RapidMiner, I found it difficult to get it to read the metadata. I wanted to use, for example, a pivot table, and it did not have the variable or the attribute names in it. There were no values. It took a long while to figure out how to do that, although it tends to do it automatically nowadays. RapidMiner is not utterly intuitive for beginners. Sometimes people have trouble distinguishing between a file in their own file system and a repository entry, and they cannot find their data. This is an area where this solution could be improved. It would be helpful to have some tutorials on communicating with Python. I found it a bit difficult at times to figure out which particular variable, or attribute, is going where in Python. It is probably a simple thing to do but I haven't mastered it yet. I'd like them to do a video on that. There are a large number of videos that are usually well-produced, but I don't think that they have one on that. Essentially, I would like to see how to communicate from RapidMiner to Python and from Python to RapidMiner. One of the things I do a lot of is looking at questionnaires where people have used Likert-type scales. I don't recommend Likert-type scales, but if they're properly produced, which is a lot of hard work and it's not usually done, they're really powerful and you can do things like normalizing holes on the Likert scale. That's not the same as normalizing your data in RapidMiner. So, I would want to get results with these Likert scales, pass it through RapidMiner, do a normalization and pass back both the raw scores and the normalized scores and put in some rules, which will say if it's high on the raw score and on the normalized score and low on the standard deviation, then you can trust it.

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RichardXu

Data Science Lead at a mining and metals company with 10,001+ employees

The platform organizes workflows visually and efficiently

One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.

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Laurence Moseley

Emeritus Professor of Health Services Research at University of South Wales

Has a drag-and-drop interface and AI capabilities

It's difficult to pinpoint one single feature because KNIME has so many. For starters, it's very easy to learn. You can get started with just a one-hour video. The drag-and-drop interface makes it user-friendly. For example, if you want to read an Excel file, drag the "read Excel file" node from the repository, configure it by specifying the file location, and run it. This gives you a table with all your data. Next, you can clean the data by handling missing values, selecting specific columns you want to analyze, and then proceeding with your analysis, such as regression or correlation. KNIME has over 4,500 nodes available for download. In addition, KNIME offers various extensions. For instance, the text processing extension allows you to process text data efficiently. It's more powerful than other tools like NVivo and Palantir. KNIME also has AI capabilities. If you're unsure about the next step, the AI assistant can suggest the most frequently used nodes based on your previous work. Another valuable feature is the integration with Python. If you need to perform a task that requires Python, you can simply add a Python node, write the necessary code,

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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 most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically."

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

"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."

"One of the most valuable features is the built-in data tuning feature. Once the model is built, we often struggle to increase its accuracy, but RapidMiner allows us to fine-tune variables. For Example, when working on a project, we can adjust the number of nodes or the depth of trees to see how accuracy changes. This flexibility lets us achieve higher accuracy compared to traditional automated machine-learning models"

"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."

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

"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 solution is stable."

More Altair RapidMiner pros

"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."

"One of the valuable features of Dataiku is the workflow capability."

"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."

"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."

"Cloud-based process run helps in not keeping the systems on while processes are running."

"I believe the return on investment looks positive."

"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."

"Data Science Studio's data science model is very useful."

More Dataiku pros

"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."

"I've never had any problems with stability."

"One of the greatest advantages of KNIME is that it can be used by those without any coding experience. those with no coding background can use it."

"It is very fast to develop solutions."

"We have found KNIME valuable when it comes to its visualization."

"Valuable features include visual workflow creation, workflow variables (parameterisation), automatic caching of all intermediate data sets in the workflow, scheduling with the server."

"It's a huge tool with machine learning features as well."

"It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea."

More KNIME pros

Cons

"I would like to see all users have access to all of the deep learning models, and that they can be used easily."

"The product must provide data-cleaning features."

"A great product but confusing in some way with regard to the user interface and integration with other tools."

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

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

"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."

"The price of this solution should be improved."

"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."

More Altair RapidMiner cons

"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."

"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."

"We still encounter some integration issues."

"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."

"I think it would help if Data Science Studio added some more features and improved the data model."

"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."

"The ability to have charts right from the explorer would be an improvement."

"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."

More Dataiku cons

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

"One area that could be improved is increasing awareness and adoption of KNIME among organizations. Despite its capabilities, it is not as well-known as other tools. The advertising and marketing efforts to reach out to companies and universities have not been very successful."

"Sometimes, we needed more space to handle larger operations, especially since our machines had limited space and memory due to Kubernetes clusters."

"In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them."

"It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge."

"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."

"The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes."

"The current UI is primarily in English. Analyzing data in local languages might present challenges or issues."

More KNIME cons

Pricing and Cost Advice

"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."

"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."

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

"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."

"Pricing is pretty steep. Dataiku is also not that cheap."

"There is no cost for using KNIME because it is an open-source solution, but you have to pay if you need a server."

"It is free of cost. It is GNU licensed."

"It is expensive to procure the license."

"KNIME is a cost-effective solution because it’s free of cost."

"KNIME is free and open source."

"I use the open-source version."

"This is an open-source solution that is free to use."

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

More KNIME pricing and cost advice

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Top Industries

By visitors reading reviews

University

11%

Computer Software Company

11%

Educational Organization

10%

Financial Services Firm

18%

Educational Organization

11%

Manufacturing Company

Computer Software Company

Financial Services Firm

12%

Manufacturing Company

11%

Computer Software Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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 started with a trial version. We are likely to purchase a license, which may offer additional features.

What needs improvement with RapidMiner?

Currently, I am unsure of all the AI features available in Altair RapidMiner, particularly advanced AI capabilities l...

What is your experience regarding pricing and costs for Dataiku Data Science Studio?

I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is...

What needs improvement with Dataiku Data Science Studio?

There is room for improvement in terms of allowing for more code-based features. I would love for Dataiku to allow mo...

What is your primary use case for Dataiku Data Science Studio?

My company sells licenses for both Dataiku and Alteryx, and we have clients who use them. I engage with several compa...

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?

I have seen the potential to interact with Python, which is currently a bit limited. I am interested in the newer ver...

Alteryx vs Altair RapidMiner

Comparisons

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Compared 3% of the time

Databricks vs Altair RapidMiner

Compared 2% of the time

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Compared 32% of the time

Alteryx vs Dataiku

Compared 12% of the time

Amazon SageMaker vs Dataiku

Compared 9% of the time

Microsoft Azure Machine Learning Studio vs Dataiku

Compared 5% of the time

Domino Data Science Platform vs Dataiku

Compared 3% of the time

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Microsoft Power BI vs KNIME

Compared 14% of the time

Alteryx vs KNIME

Compared 8% of the time

Microsoft Azure Machine Learning Studio vs KNIME

Compared 7% of the time

Weka vs KNIME

Compared 7% of the time

Databricks vs KNIME

Compared 5% of the time

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Product Reports

Altair RapidMiner

Download Altair RapidMiner product report

Download Dataiku product report

Download KNIME product report

KNIME

April 2025

Also Known As

No data available

Dataiku DSS

KNIME Analytics Platform

Overview

Altair RapidMiner is a leading platform for data science and machine learning, offering a user-friendly interface with powerful tools for predictive analytics. It supports integration with APIs, Python, and cloud services for streamlined workflow creation.

RapidMiner provides an efficient data science environment featuring drag-and-drop functionality, automation tools, and a wide array of algorithms, making it adaptable for novices and experts alike. Users benefit from easy data preparation and analysis alongside robust support from a vibrant community. Challenges include better onboarding and deep learning model accessibility, alongside calls for enhanced image processing and large language model integration.

What features make Altair RapidMiner stand out?

Data science tools: Facilitates machine learning and predictive analytics.
Code-optional GUI: Enables non-programmers to create workflows seamlessly.
Integration: Supports APIs, Python, and cloud services.
Drag-and-drop functionality: Simplifies workflow creation for users.
Algorithm variety: Offers diverse methods for data analysis.

What benefits and ROI should users consider when evaluating?

Efficiency: Saves time in data preparation and workflow automation.
Accessibility: Intuitive design aids beginners and experts.
Community: Access to extensions and collaborative resources.
Documentation: Comprehensive tutorials enhance learning.

Altair RapidMiner is extensively used in business and academia, facilitating tasks like predictive analytics, segmentation, and deployment. In education, it supports data science teaching and research, while in industries such as telecom, banking, and healthcare, it's used for data mining, decision trees, and market analysis.

Altair

Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

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

Sample Customers

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto

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

Data Science Platforms