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 19.1% 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 85 Databricks reviews

17,588 Views
11,896 Comparison Views

96% willing to recommend

RapidMiner

Read 22 RapidMiner reviews

5,158 Views
4,232 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 831,265 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

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 January 2025, in the Data Science Platforms category, the mindshare of Databricks is 19.1%, up from 18.5% compared to the previous year. The mindshare of RapidMiner is 7.7%, up from 5.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

Integrating engineering and learning, but cost challenges arise with cluster management

We often use a single cluster to ingest Databricks, which Databricks doesn't recommend. They suggest using a no-cluster solution like job clusters. This can be overwhelming for us because we started smaller. We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly. We need to stay in sync with the DVR versions, and migrations can pose challenges. For example, issues arose when we moved a cluster from a previous version to the latest one. We could use their job clusters, however, that increases costs, which is challenging for us as a startup. Maintaining this infrastructure can be a headache.

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

"Databricks has helped us have a good presence in data."

"It's great technology."

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

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

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

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

"The most valuable features of the solution are the hardware and the resources it quickly provides without much hassle."

"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."

More Databricks pros

"The best part of RapidMiner is efficiency."

"RapidMiner is very easy to use."

"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"

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

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

"The solution is very intuitive and powerful."

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

"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."

More RapidMiner pros

Cons

"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."

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

"Implementation of Databricks is still very code heavy."

"Would be helpful to have additional licensing options."

"In the next release, I would like to see more optimization features."

"The tool should improve its integration with other products."

"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."

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

More Databricks cons

"The price of this solution should be improved."

"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."

"It would be helpful to have some tutorials on communicating with Python."

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

"I would like to see more integration capabilities."

"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."

"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."

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 product pricing is moderate."

"There are different versions."

"The solution requires a subscription."

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

"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 licensing costs of Databricks is a tiered licensing regime, so it is flexible."

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

More Databricks pricing and cost advice

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

"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."

"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."

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

831,265 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

10%

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

Informatica PowerCenter vs Databricks

Compared 8% of the time

Dremio vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Microsoft Power BI 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 9% of the time

Tableau vs RapidMiner

Compared 6% of the time

DataRobot vs RapidMiner

Compared 2% of the time

More RapidMiner Competitors

Product Reports

Buyer's Guide

Databricks

January 2025

Download Databricks product report

Buyer's Guide

RapidMiner

January 2025

Download RapidMiner product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Learn More

Databricks

RapidMiner

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.

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.

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

831,265 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.