Altair RapidMiner vs Databricks comparison

Read 91 Databricks reviews

25,542 Views
11,494 Comparison Views

96% willing to recommend

Altair RapidMiner

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Jun 8, 2025

Databricks and Altair RapidMiner compete in the data processing and machine learning tools category. Databricks seems to have the upper hand in scalability and collaboration, while Altair RapidMiner excels in user-friendly no-code environments.

Features: Databricks offers strong scalability, integration capabilities, and supports multiple programming languages. It includes a comprehensive machine learning suite and high performance for big data processing. Altair RapidMiner is valued for its ease of use, intuitive interface, and powerful no-code environment, supporting various file formats and automation features that appeal to non-programmers.

Room for Improvement: Databricks could improve visualization tools, better integrate with reporting tools, and enhance error message clarity. Users note high pricing and require improved documentation. Altair RapidMiner could extend its machine learning algorithm support, enhance deep learning, and offer more affordable pricing. Usability issues and tool integration improvements are also desired.

Ease of Deployment and Customer Service: Databricks is widely used in cloud environments with strong customer support, though there are some concerns about responsiveness. Altair RapidMiner primarily offers on-premises deployment with comprehensive documentation, though technical support varies.

Pricing and ROI: Databricks has competitive pricing in cloud contexts but is expensive for resource-heavy tasks. However, it achieves positive ROI through reduced infrastructure costs. Altair RapidMiner's freemium model and free educational licenses make it accessible, but paid versions are costly, with positive ROI due to comprehensive features and competitive licensing fees.

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

Altair RapidMiner vs. Databricks

June 2025

Download the complete report

Helped 861,524 peers since 2012

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 (8th), Streaming Analytics (1st)

Mindshare comparison

As of July 2025, in the Data Science Platforms category, the mindshare of Altair RapidMiner is 7.7%, up from 7.1% compared to the previous year. The mindshare of Databricks is 15.9%, down from 19.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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.

Read full review

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.

Read full review

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

"The best part of RapidMiner is efficiency."

"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 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 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."

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

"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 Altair RapidMiner pros

"It offers AI functionalities that assist with code management and machine learning processes."

"The ease of use and its accessibility are valuable."

"The solution offers a free community version."

"We have the ability to scale, collaborate and do machine learning."

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

"We can scale the product."

"The processing capacity is tremendous in the database."

"Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours."

More Databricks pros

Cons

"I would like to see more integration capabilities."

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

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

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

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

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

"I think that they should make deep learning models easier."

"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 Altair RapidMiner cons

"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."

"Pricing is one of the things that could be improved."

"The product should provide more advanced features in future releases."

"Databricks could improve in some of its functionality."

"The integration and query capabilities can be improved."

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

"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."

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

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."

"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."

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

"The solution requires a subscription."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

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

"The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."

"Databricks' cost could be improved."

"The product pricing is moderate."

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

"The solution is affordable."

More Databricks pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

861,524 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

11%

University

10%

Educational Organization

10%

Manufacturing Company

Financial Services Firm

17%

Computer Software Company

10%

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 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 vs Altair RapidMiner

Comparisons

Compared 51% of the time

Dataiku vs Altair RapidMiner

Compared 16% of the time

Tableau vs Altair RapidMiner

Compared 8% of the time

Alteryx vs Altair RapidMiner

Compared 7% of the time

SAS Enterprise Miner vs Altair RapidMiner

Compared 2% of the time

More Altair RapidMiner Competitors

Dataiku vs Databricks

Compared 10% 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 vs Databricks

Compared 5% of the time

More Databricks Competitors

Product Reports

Download Altair RapidMiner product report

Altair RapidMiner

July 2025

Download Databricks product report

July 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