We performed a comparison between Dataiku and SAS Enterprise Miner 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."If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"The solution is quite stable."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"Data Science Studio's data science model is very useful."
"The most valuable feature is the set of visual data preparation tools."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."
"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."
"I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks."
"I like the way the product visually shows the data pipeline."
"The most valuable feature is the decision tree creation."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"he solution is scalable."
"The technical support is very good."
"The ability to have charts right from the explorer would be an improvement."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"I think it would help if Data Science Studio added some more features and improved the data model."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."
"The visualization of the models is not very attractive, so the graphics should be improved."
"Technical support could be improved."
"Virtualization could be much better."
"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."
"The product must provide better integration with cloud-native technologies."
"The initial setup is challenging if doing it for the first time."
Dataiku is ranked 11th in Data Science Platforms with 7 reviews while SAS Enterprise Miner is ranked 16th in Data Science Platforms with 13 reviews. Dataiku is rated 8.2, while SAS Enterprise Miner is rated 7.6. The top reviewer of Dataiku writes "Gives different aspects of modeling approaches and good for multiple teams' collaboration". On the other hand, the top reviewer of SAS Enterprise Miner writes "A stable product that is easy to deploy and can be used for structured and unstructured data mining". Dataiku is most compared with Databricks, KNIME, Alteryx, RapidMiner and Microsoft Azure Machine Learning Studio, whereas SAS Enterprise Miner is most compared with SAS Visual Analytics, IBM SPSS Modeler, RapidMiner, Microsoft Azure Machine Learning Studio and KNIME. See our Dataiku vs. SAS Enterprise Miner report.
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