RapidMiner is a data science and data mining platform, from Altair since the late 2022 acquisition. RapidMiner offers full automation for non-coding domain experts, an integrated JupyterLab environment for seasoned data scientists, and a visual drag-and-drop designer. RapidMiner’s project-based framework helps to ensure that others can build off their work using visual workflows or automated data science.
$7,500
Per User Per Month
SAS Enterprise Miner
Score 9.0 out of 10
N/A
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
N/A
Pricing
RapidMiner
SAS Enterprise Miner
Editions & Modules
Professional
$7,500.00
Per User Per Month
Enterprise
$15,000.00
Per User Per Month
AI Hub
$54,000.00
Per User Per Month
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Offerings
Pricing Offerings
RapidMiner
SAS Enterprise Miner
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Community Pulse
RapidMiner
SAS Enterprise Miner
Considered Both Products
RapidMiner
Verified User
Anonymous
Chose RapidMiner
We tried different data tools and we figured we give RapidMinder Studio a shot as one of our employees had experience with it, and when compared to some of the other tools that we used it was the best fit among the test group that we used. Overall it was a little more fluid and …
For me, the best advantage to use RapidMiner is the ease of use to learn and deploy new processes. Yo don't need to code, you learn fast and it's really flexible when it comes to transforming data. Knime is also good, but not so flexible, and visually less attractive. Pentaho …
The other product like RapidMiner Studio that I have used is WEKA. I decided to use RapidMiner because almost all modelling methods and feature selection methods from the Weka machine learning library are available within RapidMiner. Furthermore, RapidMiner Studio is a visual …
Used R and RapidMiner Studio. The main advantage for RapidMiner Studio is the reduced need to program. It has a much smaller learning curve, and it is easy to start using the tool and analyzing from day one.
We selected RapidMiner due to ease of use and a comfortable user interface. It stacks up very well against these tools in the predictive analytics space. For basic analytics and data reporting, we chose QlikView and Qlik Sense as a more robust reporting platform.
SPSS and SAS are too expensive. Their interfaces are excellent, but the price point is quite high making them inappropriate for higher education. KNIME is my second choice tool in this space, but it doesn't have the same long established english-speaking user community as …
The best part about RapidMiner is it mainly focus on machine learning algorithms whereas other tools focus on mainly the extract transform load (ETL) process. It can serve for all the KDD (Knowledge data discovery) process stages e.g. data cleaning, transformation, modeling and …
RapidMIner Studio is freely available and requires no programming skills. When compared with other free analytics tools, its graphical and analytical capabilities are far superior.
You simply cannot do everything with RapidMiner, it is just one tool in your arsenal. I like using Python directly much better with tools such as Jupyter Notebook in conjunction with JupyterHub.
The problem with R was that you had to code everything yourself and it doesn't do that well with large amounts of data. At the same time the advantage it provided was it has a large user base which means that you could get help easily.
For those that are used to the SAS ecosystem, SAS Enterprise Miner is a massive move in the right direction. It makes doing analytics much more enjoyable. It is more user-friendly than Spotfire or Kinesis and seems to produce better results overall. SAS Enterprise Miner …
I like the algorithms SAS uses better than SPSS. I have been writing SAS code since the mid 1980s and trust their development team. Also offer great refresher class to academics.
SAS EM has a very great set of machine learning and predictive analytics toolsets, which helped our organization achieve its goals. We used other tools, but for us, SAS EM was the most intuitive and easy to learn the tool and it provides greater data exploration and data …
SPSS was used for model development before SAS in my organization. SAS brought a bigger more complete integrated solution than SPSS had. It allowed users to easily prepare their data with SAS/Enterprise Guide and then use it with Enterprise Miner. The data preparation tools of …
RapidMiner is the best tool to build models on textual data. It is rich in ML algorithms and reduces the need to manually tune the parameters. It automatically optimizes them, thus providing a better solution. RapidMiner again extends great capability for data preparation, its insane connections to almost every data source pulls in the data easily into one environment. And it can comfortably perform data cleaning and process tasks over that. RapidMiner is not so good with image, audio or video data. These data points cannot be used directly in their raw form. They must be transformed into some intermediate form for performing analytics over it. Moreover, there are no connectors to directly pull data from their varied sources. For example, we don't have a connector to read audio data directly from a switch and then convert it to text (although Google speech API is available for audio to text conversion.)
Wish the tool was more efficient in terms of processing power. The tool takes a lot of CPU processing power, even for a small process on a small data set
Wish there were more options on charts and graphs to visualize the data
One of the major flaws is that the tool is basically an interface to SAS/STAT code. It generates code in the background and runs it. Because of that, some errors are warning might be a little difficult to understand for users who aren't proficient with SAS code.
R integration is nice but I would like to see the possibility to integrate even more statistical models different than SAS. That would allow for better performance optimization when really required.
The light client is java based and a little heavy on the OS. It would be nice to get a web-based version of the tool instead of the java one.
I have contacted SAS twice in the past year and they have been super responsive both times. They solved my problem. I am also registered for an in-person class next month and they called today to tell me that it will be an online-only session. They apologized for the change and registered me for the online version. Super helpful!
The other product like RapidMiner Studio that I have used is WEKA. I decided to use RapidMiner because almost all modelling methods and feature selection methods from the Weka machine learning library are available within RapidMiner. Furthermore, RapidMiner Studio is a visual workflow and therefore it is easier to demonstrate and visualise the processes involves in getting the desired results. Visualization of workflow enhances teaching and learning. RapidMiner is rich with algorithms and online learning materials that can assist students in their self-directed learning on data preparation, machine learning, deep learning, text mining, and predictive analytics. Moreover, RapidMiner repository has more than 1500 machine learning algorithms and functions that students can explore for any case study and assignments. The RapidMIner is also an open platform that can seamlessly integrates with other applications programmed with other programming languages like R and Python.
For those that are used to the SAS ecosystem, SAS Enterprise Miner is a massive move in the right direction. It makes doing analytics much more enjoyable. It is more user-friendly than Spotfire or Kinesis and seems to produce better results overall. SAS Enterprise Miner seems to be written by analysts for analysts.
It has a positive ROI to our business, as our sales lead rate increased after we started recommending SAS EM.
Our business operation numbers improved after we introduced SAS EM and started using predictive analytics for our customer retention and customer chain prediction.
The statistical modelling for the risk controls in our financial department helped to reduce the related residual risk.