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 11.8% 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.

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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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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of December 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.7%, up from 2.7% compared to the previous year. The mindshare of Dataiku is 11.8%, up from 7.6% compared to the previous year. The mindshare of RapidMiner is 7.7%, up from 5.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Md Masudul Hassan

Agricultural Finance and IT Researcher at Sher-e-Bangla Agricultural University

Comprehensive data analysis capabilities with a user-friendly interface, providing an efficient and reliable platform for researchers and analysts

I believe that offering short-term SPSS licenses, perhaps when customer sourcing is available, could make it more affordable. These licenses shouldn't include features tailored for universities or large sales organizations. Instead, they could offer discounts or additional facilities for smaller entities to access the software. In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options. For example, offering basic features to the first hundred users can help them become familiar with the software and its capabilities. This approach encourages users to upgrade to higher tiers as they become more experienced and require additional functionality.

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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

"You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use."

"I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well."

"The most valuable features are the small learning curve and its ability to hold a lot of data."

"The software offers consistency across multiple research projects helping us with predictive analytics capabilities."

"The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS."

"The most valuable feature is its robust statistical analysis capabilities."

"The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can into multidimensional setup space. It's the multidimensional space facility that is most useful."

"in terms of the simplicity, I think the SPSS basic can handle it."

More IBM SPSS Statistics pros

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

"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."

"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."

"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."

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

"The solution is quite stable."

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

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

More Dataiku pros

"I like not having to write all solutions from code. Being able to drag and drop controls, enables me to focus on building the best model, without needing to search for syntax errors or extra libraries."

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

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

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

"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"

"The data science, collaboration, and IDN are very, very strong."

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

"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."

More RapidMiner pros

Cons

"The product should provide more ways to import data and export results that are user-friendly for high-level executives."

"The design of the experience can be improved."

"The reports could be better."

"I would like SPSS to improve its integration with other data-filing IBM tools. I also think its duration with data, utilization, and graphics could be better."

"The solution needs more planning tools and capabilities."

"In some cases, the product takes time to load a large dataset. They could improve this particular area."

"The solution needs to improve forecasting using time series analysis."

"I think the visualization and charting should be changed and made easier and more effective."

More IBM SPSS Statistics cons

"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."

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

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

"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."

"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."

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

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

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

More Dataiku cons

"The product must provide data-cleaning features."

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

"Many things in the interface look nice, but they aren't of much use to the operator. It already has lots of variables in there."

"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 can improve deep learning by enhancing the features."

"The price of this solution should be improved."

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

"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."

More RapidMiner cons

Pricing and Cost Advice

"More affordable training for new staff members."

"The price of IBM SPSS Statistics could improve."

"It's quite expensive, but they do a special deal for universities."

"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."

"While the pricing of the product may be higher, the accompanying service and features justify the investment."

"The price of this solution is a little bit high, which was a problem for my company."

"I rate the tool's pricing a five out of ten."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

More IBM SPSS Statistics 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."

"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."

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

"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."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

University

Manufacturing Company

Financial Services Firm

18%

Educational Organization

16%

Manufacturing Company

Computer Software Company

University

12%

Computer Software Company

10%

Educational Organization

10%

Financial Services Firm

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about IBM SPSS Statistics?

The software offers consistency across multiple research projects helping us with predictive analytics capabilities.

See all answers

What is your experience regarding pricing and costs for IBM SPSS Statistics?

The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...

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What needs improvement with IBM SPSS Statistics?

IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...

See all answers

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?

One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integratin...

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. I...

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 pri...

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 a...

See all answers

Comparisons

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 16% of the time

Alteryx vs IBM SPSS Statistics

Compared 13% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 12% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 12% of the time

KNIME vs IBM SPSS Statistics

Compared 3% of the time

More IBM SPSS Statistics Competitors

Databricks vs Dataiku

Compared 37% of the time

KNIME vs Dataiku

Compared 15% of the time

Alteryx vs Dataiku

Compared 11% of the time

Amazon SageMaker vs Dataiku

Compared 6% of the time

IBM SPSS Modeler vs Dataiku

Compared 1% 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

IBM SPSS Statistics

December 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Dataiku

December 2024

Download Dataiku product report

Buyer's Guide

RapidMiner

November 2024

Download RapidMiner product report

Also Known As

SPSS Statistics

Dataiku DSS

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IBM

Dataiku

RapidMiner

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

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

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

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

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

Buyer's Guide

Dataiku vs. RapidMiner

December 2024

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