Cloudera Data Science Workbench vs RapidMiner comparison

Cloudera Data Science Workb...

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Executive SummaryUpdated on Dec 5, 2024

RapidMiner and Cloudera Data Science Workbench compete in the data science platform space. RapidMiner has the upper hand due to its user-friendly pricing and ease of use, while Cloudera is appreciated for its advanced features.

Features: RapidMiner provides tools for visual workflow design, automation, and machine learning operations, appealing to users who prefer minimal coding. Cloudera Data Science Workbench offers tools for coding-intensive environments, supporting Python, R, and Scala, emphasizing flexibility and integration.

Ease of Deployment and Customer Service: RapidMiner's straightforward deployment and comprehensive support services enable quick implementation. Cloudera Data Science Workbench requires advanced IT skills for deployment but benefits from a robust support network, essential for complex projects.

Pricing and ROI: RapidMiner is more cost-effective with lower setup costs, whereas Cloudera justifies higher costs with advanced features, providing significant ROI for data-intensive operations.

To learn more, read our detailed Cloudera Data Science Workbench vs. RapidMiner Report (Updated: January 2025).

Cloudera Data Science Workbench vs. RapidMiner

January 2025

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Helped 838,713 peers since 2012

Review summaries and opinions

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

Categories and Ranking

Cloudera Data Science Workb...

Ranking in Data Science Platforms

22nd

Average Rating

7.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

RapidMiner

Ranking in Data Science Platforms

6th

Average Rating

8.6

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Predictive Analytics (3rd)

Mindshare comparison

As of February 2025, in the Data Science Platforms category, the mindshare of Cloudera Data Science Workbench is 1.4%, down from 1.7% compared to the previous year. The mindshare of RapidMiner is 7.7%, up from 6.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Ismail Peer

Program Management Lead Advisor at Unionbank Philippines

Useful for data science modeling but improvement is needed in MLOps and pricing

If you don't configure CDSW well, then it might be not useful for you. Deploying the tool can vary in complexity, but most of the time, it's relatively simple and straightforward. Triggering a job from data to production is easy, as the platform automates the deployment process. However, ensuring optimal resource allocation is essential for smooth operations.

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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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Quotes from Members

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

Pros

"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."

"The Cloudera Data Science Workbench is customizable and easy to use."

"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."

"It's helpful if you want to make informed decisions using data. We can take the information, tease out the attributes, and label everything. It's suitable for profiling and forecasting in any industry."

"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"

"What I like about RapidMiner is its all-in-one nature, which allows me to prepare, extract, transform, and load data within the same tool."

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

"I've been using a lot of components from the Strategic Extension and Python Extension."

"The solution is very intuitive and powerful."

More RapidMiner pros

Cons

"Running this solution requires a minimum of 12GB to 16GB of RAM."

"The tool's MLOps is not good. It's pricing also needs to improve."

"In the Mexican or Latin American market, it's kind of pricey."

"I would appreciate improvements in automation and customization options to further streamline processes."

"About twenty-five percent of my problems involve image processing, and I found RapidMiner lacking in this domain. While we work on OCR and similar tasks, RapidMiner hasn't been as engaged in that field as other models. Some other models also support email processing, but RapidMiner doesn't offer this feature."

"The price of this solution should be improved."

"It would be helpful to have some tutorials on communicating with Python."

"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."

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

"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."

More RapidMiner cons

Pricing and Cost Advice

"The product is expensive."

"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."

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

33%

Manufacturing Company

12%

Healthcare Company

Computer Software Company

University

12%

Computer Software Company

11%

Financial Services Firm

10%

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Cloudera Data Science Workbench?

I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy...

What needs improvement with Cloudera Data Science Workbench?

The tool's MLOps is not good. It's pricing also needs to improve.

What is your primary use case for Cloudera Data Science Workbench?

We have different use cases. Our banking use case uses machine learning to identify customer life events and recommend the best-suited card products. These machine-learning models are deployed in o...

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. It can also connect to databases, allowing me to build models directly on the dat...

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 priced, not too high or too low. It's worth the investment.

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 and had to use Python instead.

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Product Reports

Download Cloudera Data Science Workbench product report

Data Science Platforms

January 2025

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February 2025

Also Known As

CDSW

No data available

Overview

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. Access any data anywhere – from cloud object storage to data warehouses, CDSW provides connectivity not only to CDH but the systems your data science teams rely on for analysis.

Cloudera

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.

Sample Customers

IQVIA, Rush University Medical Center, Western Union

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen