RapidMiner vs. SAS Enterprise Guide

RapidMiner is really fantastic to perform fast ETL processes and work on your data as you want, no matter what is the source. You will really save a lot of time when you learn how to use it. You can create mining analysis with several algorithms, and thanks to add-ons, you can apply a lot of techniques. It will not replace a business intelligence dashboard but it allows to create great datamarts for your BI tools. One negative thing is that It's no easy to share your outputs.

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SAS Enterprise Guide is good at taking various datasets and giving analyst/user ability to do some transformations without substantial amounts of code. Once the data is inside SAS, the memory of it is very efficient. Using SAS for data analysis can be helpful. It will give good statistics for you, and it has a robust set of functions that aid analysis.

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• I am very impressed at how easily you can work within RapidMiner without much data analytics training. Plus with the help of the crowd, you can see what steps others have taken with their data analytics projects.
• Text mining was simple and clean. We used this for our call transcription problem where we didn't have the resources to listen to each call. We needed to qualify each call based on some key phrases.
• Our direct mail program was large and not very targeted. Using RapidMiner, we were able to isolate a predictive level we felt comfortable with and decided not to send to anyone below that level. We saved quite a bit of money.

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• Ability to load an AutoExec when opening a session ensuring everyone has the same global variables.
• Formatting with Ctrl I. If you're reading someone else's code and it's not formatted correctly you can highlight the area and hit Ctrl I.

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• I hope RapidMiner would be the first data science platform that allows data scientists to change the behaviour of a machine learning algorithm that already exists in the repository. For example, I want to be able to change the way a genetic algorithm mutates.
• Automatic programming: One day, I hope RapidMiner can automatically generate codes in any 4th generation programming language based on the developed model.
• More tutorials/samples needed: Why doesn't RapidMiner becomes the next 'UC Irvine Machine Learning Repository'? Provide real examples and real cases for users to study and understand the best practices in modelling. RapidMiner already has some datasets for a tutorial. Besides the existing samples, I hope RapidMiner can provide more sample data and examples.

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• Process time of data is a bit long. It depends on the size of your data and complexity of your project tree.
• There is not enough online free training videos.
• While working with the project tree sometimes the links between the modules are broken or the order for running the modules get mixed up. You should know your project tree by heart.

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Very fast and user-friendly tool

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On account of current user experience and the organization-wide acceptance.

Very use to use and learn

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It's not all bad, but I don't believe that an enterprise purchase of SAS is worth the expense considering the widely available set of tools in the data analytics space at the moment. In my company, it's a good tool because others use it. Otherwise, I wouldn't purchase a new set of it because it doesn't have some of the better analytical functions in it.

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Although I use SAS support for information on functions, these are SAS related and haven't really come across anything that is specifically for SAS EG.

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I've not worked hands-on with the implementation team, but there were no escalations barring a few hiccups in the deployment due to change in requirement & adoption to our company's remote servers.

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 user-friendly.

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Why I prefer SAS EG: Data processing speed is much faster than that R Studio. It can load any amount of data and any type of data like structured or unstructured or semi-structured. Its output delivery system by which we have the output in PDF file makes it very comfortable to use and share that file to clients very easily. Inbuilt functions are very powerful and plentiful. Facility of writing macros makes it far away from its competitors.

• Thanks to the patters that RapidMiner has detected, we have been able to follow clues in the right direction, both for the Protein Interaction Network Analysis and for the Epilepsy Research
• Students and participants of the machine learning workshops have learned about this technology and about the tool

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• Positive (cost): SAS made a bundle that include unlimited usage of SAS/Enterprise Guide with a server solution. That by itself made the company save a lot of money by not having to pay individual licences anymore.
• Positive (insight): Data analysts in business units often need to crunch data and they don't have access to ETL tools to do it. Having access to SAS/EG gives them that power.
• Positive (time to market): Having the users develop components with SAS/EG allows for easier integration in a production environment (SAS batch job) as no code rework is required.