Databricks vs RapidMiner comparison

Databricks and RapidMiner are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.3, while RapidMiner is ranked #6 with an average rating of 8.3. Databricks holds a 18.8% mindshare in Data Science Platforms, compared to RapidMiner’s 7.7% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 95% of RapidMiner users who would recommend it.

Databricks

Read 88 Databricks reviews

16,774 Views
11,473 Comparison Views

96% willing to recommend

RapidMiner

Read 22 RapidMiner reviews

5,303 Views
4,199 Comparison Views

95% willing to recommend

Databricks

RapidMiner

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 5, 2024

Databricks and RapidMiner are leading players in the analytics software industry, each offering distinct advantages. Databricks takes the lead with its extensive machine learning capabilities and robust big data functionalities, making it ideal for large-scale data handling. RapidMiner stands out for its no-code platform, simplifying data processing for those with limited scripting expertise.

Features: Databricks offers advanced machine learning and big data capabilities, seamless integration with platforms like Spark, and extensive collaboration options on its open-source platform. RapidMiner provides a no-code interface for accessible data processing, drag-and-drop functionalities for model-building, and straightforward deployment management.

Room for Improvement: Databricks could enhance its pricing structure, integration options, and machine learning library usability. Improving visualization features and adding support for complex deep learning could also be beneficial. RapidMiner should focus on expanding its deep learning capabilities, incorporating more advanced machine learning algorithms, and improving UI integration and onboarding resources.

Ease of Deployment and Customer Service: Databricks supports flexible deployments across public, private, and hybrid clouds, offering scalability but with potential complexity for newcomers; technical support is generally well-regarded despite some delays. RapidMiner deploys primarily on-premises or private cloud with excellent documentation that reduces support necessity, and users rate the support they receive as satisfactory.

Pricing and ROI: Databricks employs a pay-per-use model which can lead to high costs for large data volumes, though its comprehensive features offer substantial ROI. RapidMiner’s pricing is more approachable with a freemium option for academic use, making it cost-effective for new users, although it can be expensive in some markets.

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

Buyer's Guide

Databricks vs. RapidMiner

January 2025

Download the complete report

Helped 838,713 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

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)

RapidMiner

Ranking in Data Science Platforms

6th

Average Rating

8.6

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Predictive Analytics (3rd)

Mindshare comparison

As of February 2025, in the Data Science Platforms category, the mindshare of Databricks is 18.8%, up from 18.5% compared to the previous year. The mindshare of RapidMiner is 7.7%, up from 6.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

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

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

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The setup is quite easy."

"It can send out large data amounts."

"Ability to work collaboratively without having to worry about the infrastructure."

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

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

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

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

"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."

More Databricks pros

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

"The most valuable features are the Binary classification and Auto Model."

"The best part of RapidMiner is efficiency."

"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."

"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."

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

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

"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."

More RapidMiner pros

Cons

"The integration and query capabilities can be improved."

"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."

"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."

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

"This solution only supports queries in SQL and Python, which is a bit limiting."

"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."

"The integration of data could be a bit better."

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

More Databricks cons

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

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

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

"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."

"Improve the online data services."

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

"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."

More RapidMiner cons

Pricing and Cost Advice

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

"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."

"The pricing depends on the usage itself."

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

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

"Databricks' cost could be improved."

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

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

More Databricks pricing and cost advice

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

"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."

"The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license."

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

838,713 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

University

12%

Computer Software Company

11%

Financial Services Firm

10%

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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 ...

See all answers

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...

See all answers

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...

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. It can also connect to databases, allowing me to build models directly on the dat...

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 priced, not too high or too low. It's worth the investment.

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 and had to use Python instead.

See all answers

Comparisons

Dataiku vs Databricks

Compared 11% of the time

Dremio vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 7% of the time

More Databricks Competitors

KNIME vs RapidMiner

Compared 51% of the time

Dataiku vs RapidMiner

Compared 16% of the time

Alteryx vs RapidMiner

Compared 8% of the time

Tableau vs RapidMiner

Compared 7% of the time

DataRobot vs RapidMiner

Compared 2% of the time

More RapidMiner Competitors

Product Reports

Buyer's Guide

Databricks

February 2025

Download Databricks product report

Buyer's Guide

RapidMiner

February 2025

Download RapidMiner product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Overview

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.

Databricks

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.

RapidMiner

Sample Customers

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

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

Buyer's Guide

Databricks vs. RapidMiner

January 2025

Free Report: Databricks vs. RapidMiner

Find out what your peers are saying about Databricks vs. RapidMiner and other solutions. Updated: January 2025.

DOWNLOAD NOW

838,713 professionals have used our research since 2012.

See our Databricks vs. RapidMiner report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.