We performed a comparison between Dataiku and SAS Visual Analytics based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."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."
"The solution is quite stable."
"The most valuable feature is the set of visual data preparation tools."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"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."
"Data Science Studio's data science model is very useful."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"It integrates well with SAS, making it simple and quick for developers."
"The flexibility of the configuration is valuable to me."
"The speed to display charts and react to users' choices is great."
"I use Visual Analytics for enterprise reporting."
"The alert generation feature also helps in sending out ad hoc messages to the business users if business thresholds have been crossed."
"It's quite easy to learn and to progress with SAS from an end-user perspective."
"It's a stable, reliable product."
"Quick deployment to dashboards and analytics features (using SAS Visual Statistics and Enterprise Guide). Easy to create a simple forecast and discover business insights using segmentation tools."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"The ability to have charts right from the explorer would be an improvement."
"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."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"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."
"I think it would help if Data Science Studio added some more features and improved the data model."
"In Brazil, there are few documents, courses, and other resources for studying and implementing the tool."
"It is not as mature as competitors such as Tableau and QlikView."
"The deployment isn't smooth. Deploying Visual Analytics on the cloud takes a lot of work, or you can use some providers that give you SAS as a service. For example, there is a provider called SaasNow. They host SAS Visual Analytics and the license. You can buy the license and deploy it there without the hassle of installation because deploying the software isn't easy."
"The reason we haven't rolled it out across the board is due to the fact that the licensing is so expensive."
"The solution should improve its graphics."
"Better connectivity with other data origins, better visualization, and the ability to create KPIs directly would all help."
"SAS Visual Analytics could improve by making it more accessible for users outside the organization."
"The charts and tables could use better sorting, primarily using other variables than the ones on the figure. If they could implement views like in the older version (previous to Viya), it would be very nice."
Dataiku is ranked 7th in Data Science Platforms with 7 reviews while SAS Visual Analytics is ranked 8th in Data Visualization with 36 reviews. Dataiku is rated 8.2, while SAS Visual Analytics is rated 8.2. 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 Visual Analytics writes "Single environment for multiple phases saves us time, and has good visualizations". Dataiku is most compared with Databricks, KNIME, Alteryx, RapidMiner and H2O.ai, whereas SAS Visual Analytics is most compared with Tableau, Microsoft Power BI, Databricks, Microsoft Azure Machine Learning Studio and SAS Enterprise Miner.
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