Dataiku vs RapidMiner comparison

Dataiku and RapidMiner are both solutions in the Data Science Platforms category. Dataiku is ranked #7 with an average rating of 7.5, while RapidMiner is ranked #6 with an average rating of 8.3. Dataiku holds a 12.1% mindshare in Data Science Platforms, compared to RapidMiner’s 7.7% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 95% of RapidMiner users who would recommend it.

Dataiku

Read 9 Dataiku reviews

7,151 Views
5,935 Comparison Views

100% willing to recommend

RapidMiner

Read 22 RapidMiner reviews

5,158 Views
4,232 Comparison Views

95% willing to recommend

Dataiku

RapidMiner

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

RapidMiner and Dataiku compete in the advanced analytics platform category. RapidMiner holds an upper hand in ease of use and cost-effectiveness, while Dataiku excels with its comprehensive feature set.

Features: RapidMiner provides an intuitive drag-and-drop interface, strong machine learning capabilities, and a wide range of operators for data manipulation. It supports multiple file formats and offers seamless integration with Python and R. Users appreciate its Auto Model feature for quick prototyping. Dataiku offers a unified data pipeline, which streamlines data preparation, integration, and visualization. It includes built-in data tuning and feature selection tools, supports multiple coding languages, and provides comprehensive recipes for data preparation.

Room for Improvement: RapidMiner could expand its integration capabilities with other enterprise platforms and enhance its scalability options for larger organizations. Its graphical interface may require improvements for more advanced customization. The documentation, while extensive, could benefit from more real-world use cases. Dataiku's initial costs may be prohibitive for smaller businesses, and its interface can be complex for beginners. Its reliance on a strong IT infrastructure might be challenging for teams with limited resources, and real-time collaboration features could be expanded.

Ease of Deployment and Customer Service: RapidMiner offers a straightforward deployment process suitable for analytics teams integrating it into existing systems. Its customer service is efficient but could benefit from more personalized support options. Dataiku provides strong integration capabilities across various platforms, tailored to meet enterprise needs. Its customer service is appreciated for its depth and responsiveness, making onboarding smoother for new users.

Pricing and ROI: RapidMiner's competitive pricing makes it a viable option for businesses looking for an economical entry into data science, with potential for quicker ROI. Dataiku's higher initial investment is balanced by comprehensive features that enable robust data strategies, potentially delivering significant returns through process automation and scalability for long-term solutions.

To learn more, read our detailed Dataiku vs. RapidMiner Report (Updated: December 2024).

Buyer's Guide

Dataiku vs. RapidMiner

December 2024

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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:

Categories and Ranking

Dataiku

Ranking in Data Science Platforms

7th

Average Rating

8.0

Reviews Sentiment

7.2

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 January 2025, in the Data Science Platforms category, the mindshare of Dataiku is 12.1%, up from 7.8% compared to the previous year. The mindshare of RapidMiner is 7.7%, up from 5.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Sabrine Bendimerad

Data Scientist at Electricite De France

Saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms

One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to use GitHub with Dataiku, in practice, it was difficult to manage our code effectively and push it from Dataiku to GitHub. Another limitation was its ability to handle different types of data. While Dataiku is powerful for working with structured data, like regular or geospatial data, it struggled with more complex data types such as text and image. In addition to the challenges with GitHub integration, the limited support for diverse data types was another feature lacking at that time.

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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

"The most valuable feature is the set of visual data preparation tools."

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

"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."

"I rate the overall product as eight out of ten."

"The solution is quite stable."

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

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

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

More Dataiku pros

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

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

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

"We value the collaboration and governance features because it's a comprehensive platform that covers everything from data extraction to modeling operations in the ML language. RapidMiner is competitive in the ML space."

"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 solution is stable."

"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."

"The most valuable features are the Binary classification and Auto Model."

More RapidMiner pros

Cons

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

"The license is very expensive."

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

"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."

"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."

"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."

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

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

More Dataiku cons

"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."

"RapidMiner isn't cheap. It's a complete solution, but it's costly."

"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."

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

"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."

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

"The price of this solution should be improved."

More RapidMiner cons

Pricing and Cost Advice

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

"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."

"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'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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

16%

Manufacturing Company

Computer Software Company

University

12%

Computer Software Company

10%

Financial Services Firm

10%

Educational Organization

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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

It is very expensive.

See all answers

What needs improvement with Dataiku Data Science Studio?

See all answers

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

We use the solution for data science and machine learning.

See all answers

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...

See all answers

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.

See all answers

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.

See all answers

Comparisons

Databricks vs Dataiku

Compared 36% of the time

KNIME vs Dataiku

Compared 16% of the time

Alteryx vs Dataiku

Compared 11% of the time

Amazon SageMaker vs Dataiku

Compared 7% of the time

Microsoft Azure Machine Learning Studio vs Dataiku

Compared 6% of the time

More Dataiku Competitors

KNIME vs RapidMiner

Compared 51% 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

IBM SPSS Modeler vs RapidMiner

Compared 3% of the time

More RapidMiner Competitors

Product Reports

Buyer's Guide

Dataiku

January 2025

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Buyer's Guide

RapidMiner

January 2025

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Also Known As

Dataiku DSS

No data available

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Dataiku

RapidMiner

Overview

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.

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

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

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

Buyer's Guide

Dataiku vs. RapidMiner

December 2024

Free Report: Dataiku vs. RapidMiner

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