Altair RapidMiner vs Databricks comparison

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Altair RapidMiner

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Executive SummaryUpdated on Sep 14, 2025

In the realm of data analytics platforms, Databricks and Altair RapidMiner both offer robust solutions, yet they cater to different audiences. Databricks holds an advantage with enterprise users seeking scalability and advanced data processing capabilities via its Spark-based infrastructure.

Features: Databricks excels in handling large-scale data analytics, providing a Spark-based environment that offers quick data processing and scalability. It supports machine learning tasks efficiently and includes collaborative notebooks and integrated machine learning libraries. Altair RapidMiner, on the other hand, prioritizes ease of use with its no-code interface, supporting extensive data preparation and delivering initial insights through machine learning and AI tools, which is particularly beneficial for users without programming skills.

Room for Improvement: Databricks could improve by enhancing its visualization capabilities, integrating better with business intelligence tools like Power BI and Tableau, and expanding its machine learning library offerings. Users also suggest enhancing documentation and addressing pricing concerns. Altair RapidMiner would benefit from integrating more machine learning algorithms, enhancing interface support, and improving documentation and support for complex integrations.

Ease of Deployment and Customer Service: Databricks provides robust support for both public and hybrid cloud environments, offering flexible deployment options that suit varying business needs. Its customer service is generally well-received, though response times could be quicker. Altair RapidMiner primarily offers on-premises deployment, limiting flexibility but is praised for its clear documentation that eases initial setup and reduces reliance on technical support.

Pricing and ROI: Databricks faces criticism for high costs, though its pay-per-use model offers value for large workloads and satisfactory ROI due to reduced data processing times. Smaller users, however, may find it expensive. Altair RapidMiner presents a more attractive pricing model with a free option for smaller datasets and competitive rates for professional licenses, appealing to educators and smaller businesses seeking cost-effective advanced analytics solutions.

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

Altair RapidMiner vs. Databricks

September 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:

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 (9th), Streaming Analytics (1st)

Mindshare comparison

As of October 2025, in the Data Science Platforms category, the mindshare of Altair RapidMiner is 7.0%, down from 7.5% compared to the previous year. The mindshare of Databricks is 13.9%, down from 19.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Databricks	13.9%
Altair RapidMiner	7.0%
Other	79.1%

Data Science Platforms

Featured Reviews

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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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

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

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

"The best part of RapidMiner is efficiency."

"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."

"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."

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

"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."

"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."

More Altair RapidMiner pros

"The time travel feature is the solution's most valuable aspect."

"What I like about Databricks is that it's one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that."

"The most valuable feature is the ability to use SQL directly with Databricks."

"Easy to use and requires minimal coding and customizations."

"Databricks is a scalable solution. It is the largest advantage of the solution."

"The ability to stream data and the windowing feature are valuable."

"The solution is built from Spark and has integration with MLflow, which is important for our use case."

"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."

More Databricks pros

Cons

"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."

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

"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."

"If they could include video tutorials, people would find that quite helpful."

"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 would like to see more integration capabilities."

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

"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

"Performance could be improved."

"My experience with the pricing and licensing model is that it remains relatively expensive. Though it's less expensive than AWS, we still need a more cost-effective solution."

"Doesn't provide a lot of credits or trial options."

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

"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."

"While Databricks is generally a robust solution, I have noticed a limitation with debugging in the Delta Live Table, which could be improved."

"The integration and query capabilities can be improved."

"Implementation of Databricks is still very code heavy."

More Databricks cons

Pricing and Cost Advice

"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."

"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."

"The price of Databricks is reasonable compared to other solutions."

"The basic version of this solution is now open-source, so there are no license costs involved. However, there is a charge for any advanced functionality and this can be quite expensive."

"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."

"The cost is around $600,000 for 50 users."

"Price-wise, I would rate Databricks a three out of five."

"Databricks' cost could be improved."

"I would rate the tool’s pricing an eight out of ten."

"There are different versions."

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

University

11%

Computer Software Company

11%

Manufacturing Company

10%

Educational Organization

Financial Services Firm

18%

Computer Software Company

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	5
Large Enterprise	8

By reviewers
Company Size	Count
Small Business	25
Midsize Enterprise	12
Large Enterprise	56

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 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 like neural networks and deep learning. It would be beneficial if the platform co...

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 Business Hub vs Altair RapidMiner

Comparisons

Compared 47% of the time

Dataiku vs Altair RapidMiner

Compared 14% of the time

Tableau Enterprise vs Altair RapidMiner

Compared 8% of the time

Alteryx vs Altair RapidMiner

Compared 6% of the time

DataRobot vs Altair RapidMiner

Compared 2% of the time

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Microsoft Power BI vs Databricks

Compared 9% of the time

Dataiku vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Dremio vs Databricks

Compared 5% of the time

Tableau Enterprise vs Databricks

Compared 4% of the time

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Product Reports

Download Altair RapidMiner product report

Altair RapidMiner

October 2025

Download Databricks product report

October 2025

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 offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

Sample Customers

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

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