We performed a comparison between Databricks and RapidMiner based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The ease of use and its accessibility are valuable."
"The solution is easy to use and has a quick start-up time due to being on the cloud."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."
"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."
"Databricks' most valuable feature is the data transformation through PySpark."
"The most valuable feature of Databricks is the integration with Microsoft Azure."
"Databricks is a scalable solution. It is the largest advantage of the solution."
"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'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."
"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."
"RapidMiner for Windows is an excellent graphical tool for data science."
"RapidMiner is very easy to use."
"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."
"The most valuable features are the Binary classification and Auto Model."
"The data science, collaboration, and IDN are very, very strong."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."
"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."
"The Databricks cluster can be improved."
"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."
"Would be helpful to have additional licensing options."
"I would like more integration with SQL for using data in different workspaces."
"The pricing of Databricks could be cheaper."
"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."
"Many things in the interface look nice, but they aren't of much use to the operator. It already has lots of variables in there."
"I think that they should make deep learning models easier."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"Improve the online data services."
"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 would like to see all users have access to all of the deep learning models, and that they can be used easily."
"I would like to see more integration capabilities."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while RapidMiner is ranked 6th in Data Science Platforms with 20 reviews. Databricks is rated 8.2, while RapidMiner is rated 8.6. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of RapidMiner writes "A no-code tool that helps to build machine learning models ". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku, Tableau and Microsoft Power BI. See our Databricks vs. RapidMiner report.
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