Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.
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.
I think the most useful aspect of SAS Enterprise Guide is the ability to use a point-and-click interface to create graphics, transform data, and perform statistics. The best part is that SAS Enterprise Guide creates base SAS code from the process, making it easy to reproduce analyses.
SAS Enterprise Guide makes creating summary statistics about as easy as it gets. If one doesn't know proc means or proc tabulate, one can use SAS Enterprise Guide instead.
The time-series forecasting procedures within SAS Enterprise Guide produce fairly good results. SAS Enterprise Guide makes time-series model comparisons relatively straight-forward.
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
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.
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.
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.
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.
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
SAS EG has better Graphical User Interface to build project trees and help users to create data queries/calculations. SAS EG can handle bigger data sets compared to other programs. You can easily clean the data sets and manipulate the data. It is easier to send the project tree to other users. However SAS EG has less free online training material over internet.
