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: July 2025).

Altair RapidMiner vs. Databricks

July 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 (2nd)

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 September 2025, in the Data Science Platforms category, the mindshare of Altair RapidMiner is 7.4%, down from 7.5% compared to the previous year. The mindshare of Databricks is 14.5%, down from 19.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Databricks	14.5%
Altair RapidMiner	7.4%
Other	78.1%

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

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

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

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

"Using the GUI, I can have models and algorithms drag and drop nodes."

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

"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."

"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"

More Altair RapidMiner pros

"The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."

"Databricks is definitely a very stable product and reliable."

"I would rate them ten out of ten."

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

"It helps integrate data science and machine learning capabilities."

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

"It is a cost-effective solution."

"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."

More Databricks pros

Cons

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

"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."

"Currently, I am unsure of all the AI features available in Altair RapidMiner, particularly advanced AI capabilities like neural networks and deep learning."

"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."

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

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

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

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

More Altair RapidMiner cons

"The biggest problem associated with the product is that it is quite pricey."

"I think setting up the whole account for one person and giving access are areas that can be difficult to manage and should be made a little easier."

"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."

"There are no direct connectors — they are very limited."

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

"As a data engineer, I see cluster failure in our Databricks user databases as a major issue."

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

"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."

More Databricks cons

Pricing and Cost Advice

"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."

"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."

"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."

"The billing of Databricks can be difficult and should improve."

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

"We're charged on what the data throughput is and also what the compute time is."

"I rate the price of Databricks as eight out of ten."

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

"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."

"The price is okay. It's competitive."

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

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

University

11%

Computer Software Company

10%

Manufacturing Company

10%

Educational Organization

10%

Financial Services Firm

17%

Computer Software Company

10%

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

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Dataiku vs Altair RapidMiner

Compared 15% of the time

Tableau Enterprise vs Altair RapidMiner

Compared 8% of the time

Alteryx vs Altair RapidMiner

Compared 6% of the time

Microsoft Power BI vs Altair RapidMiner

Compared 2% of the time

More Altair RapidMiner Competitors

Dataiku vs Databricks

Compared 9% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Dremio vs Databricks

Compared 6% of the time

Tableau Enterprise vs Databricks

Compared 5% of the time

More Databricks Competitors

Product Reports

Download Altair RapidMiner product report

Altair RapidMiner

September 2025

Download Databricks product report

September 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 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