Altair RapidMiner vs Databricks comparison

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Altair RapidMiner

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Executive SummaryUpdated on Mar 4, 2025

Databricks and Altair RapidMiner compete in the data analytics and machine learning space. Databricks appears to have an advantage due to its high scalability, ease of use with SQL and Python, and flexibility across diverse environments.

Features: Databricks supports large-scale analytics with built-in optimization recommendations and the Delta data format for quick insights. It offers flexibility in programming languages like SQL and Python and enables collaborative workspaces. RapidMiner is recognized for its intuitive drag-and-drop interface, robust machine learning algorithms, and extensive GUI features that benefit users seeking simplicity in data science applications.

Room for Improvement: Databricks could enhance its visualization and improve user-friendliness, especially concerning integration and error handling. It also faces challenges with complex pricing models and lacks comprehensive predictive analysis libraries. RapidMiner could improve in software performance speed, integration for specific data types like time-series, and metadata handling capabilities.

Ease of Deployment and Customer Service: Databricks offers high versatility with availability on public, private, and hybrid clouds, boasting solid customer service, though documentation for scaling could improve. RapidMiner's deployment is focused on-premises or private cloud, less flexible for diverse environments. While its documentation is clear, technical support experiences vary.

Pricing and ROI: Databricks utilizes a pay-per-use model which is economical for high-volume usage but can be costly for smaller operations, with ROI driven by cloud efficiency gains. RapidMiner provides a freemium model for smaller datasets, offering affordability for individuals and educational institutions, yet noted as pricey for enterprises, both demonstrating substantial ROI in processing efficiency.

To learn more, read our detailed Altair RapidMiner vs. Databricks Report (Updated: March 2025).

Altair RapidMiner vs. Databricks

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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

Altair RapidMiner

Ranking in Data Science Platforms

7th

Average Rating

8.6

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Predictive Analytics (3rd)

Databricks

Ranking in Data Science Platforms

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th), Streaming Analytics (1st)

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Altair RapidMiner is 7.7%, up from 6.5% compared to the previous year. The mindshare of Databricks is 18.2%, down from 19.1% 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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ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

Capability to integrate diverse coding languages in a single notebook greatly enhances workflow

Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.

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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 best part of RapidMiner is efficiency."

"Altair RapidMiner is appreciated for its ease of use and the CRISP data mining model it supports, covering steps like data preparation, data understanding, and business understanding."

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

"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."

"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."

"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."

More Altair RapidMiner pros

"The simplicity of development is the most valuable feature."

"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."

"The initial setup phase of Databricks was good."

"The load distribution capabilities are good, and you can perform data processing tasks very quickly."

"It is fast, it's scalable, and it does the job it needs to do."

"I haven't heard about any major stability issues. At this time I feel like it's stable."

"It is a cost-effective solution."

"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."

More Databricks pros

Cons

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

"The product must provide data-cleaning features."

"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 would like to see more integration capabilities."

"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."

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

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

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

More Altair RapidMiner cons

"They release patches that sometimes break our code. These patches are supposed to fix issues, but sometimes they cause disruptions."

"It should have more compatible and more advanced visualization and machine learning libraries."

"The Databricks cluster can be improved."

"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."

"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."

"Can be improved by including drag-and-drop features."

"The integration and query capabilities can be improved."

"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."

More Databricks cons

Pricing and Cost Advice

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

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

"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."

"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."

"Databricks uses a price-per-use model, where you can use as much compute as you need."

"It is an expensive tool. The licensing model is a pay-as-you-go one."

"We only pay for the Azure compute behind the solution."

"Databricks are not costly when compared with other solutions' prices."

"The product pricing is moderate."

"Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."

"The solution is a good value for batch processing and huge workloads."

"I would rate Databricks' pricing seven out of ten."

More Databricks 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

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

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. 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?

Altair RapidMiner needs updates to its examples, particularly in business and marketing areas, and to the tool itself. The user interface should be improved. Incorporating generative AI as an AI as...

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

KNIME vs Altair RapidMiner

Comparisons

Compared 52% of the time

Dataiku vs Altair RapidMiner

Compared 16% of the time

Alteryx vs Altair RapidMiner

Compared 8% of the time

Tableau vs Altair RapidMiner

Compared 7% of the time

DataRobot vs Altair RapidMiner

Compared 2% of the time

More Altair RapidMiner Competitors

Dataiku vs Databricks

Compared 11% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Dremio vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Tableau vs Databricks

Compared 5% of the time

More Databricks Competitors

Product Reports

Altair RapidMiner

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Download Databricks product report

Also Known As

No data available

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

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

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Sample Customers

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

Altair RapidMiner vs. Databricks