Likelihood to Recommend It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
Read full review When data is in a system that needs a complex transformation to be usable for an average user. Such tasks as data residing in systems that have very different connection speeds. It can be integrated and used together after passing through the SAS Data Integration Studio removing timing issues from the users' worries. A part that is perhaps less appropriate is getting users who are not familiar with the source data to set up the load processes.
Read full review Pros Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc. SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly. Enforced best-practices set up POCs for deployment in production with a minimum of re-work. Estimator validation lets data scientists test and prove different models. Read full review SAS/Access is great for manipulating large and complex databases. SAS/Access makes it easy to format reports and graphics from your data. Data Management and data storage using the Hadoop environment in SAS/Access allows for rapid analysis and simple programming language for all your data needs. Read full review Cons The cost is steep and so only companies with resources can afford it It will be nice to have Chinese versions so that Chinese engineers can also use it easily It takes a while to learn how to input different kinds of skin defects for detection Read full review Requires third-party drivers to connect to common data sources like SFDC, MS SQL, Postgres. Debugging errors from the logs is a complicated process. E-mail alert system is very primitive and needs customization to make it more modern, Cannot send SMS alerts for jobs. Read full review Likelihood to Renew because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
Read full review We are happy with the software and its functionality. As a SAS-shop, DataFlux is a logical choice for complex data integration.
Read full review Usability The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
Read full review The main negative point is the use of a non-standard language for customizations, as well as the poor integration with non-SAS systems. However, there is no doubt that it is a high-performance and powerful product capable of responding optimally to certain requirements.
Read full review Reliability and Availability From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
Read full review Performance Never had slow response even on our very busy network
Read full review It worked as expected.
Read full review Support Rating I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
Read full review With SAS, you pay a license fee annually to use this product. Support is incredible. You get what you pay for, whether it's SAS forums on the SAS support site, technical support tickets via email or phone calls, or example documentation. It's not open source. It's documented thoroughly, and it works.
Read full review In-Person Training The trainers on the job are very smart with solutions and very able in teaching
Read full review Online Training The Platform is very handy and suggests further steps according my previous interests
Read full review Implementation Rating It surprised us with unpredictable case of use and brand new points of view
Read full review Alternatives Considered The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
Read full review Because of ease of using SAS DI and data processing speed. There were lots of issues with AWS Redshift on cloud environment in terms of making connections with the data sources and while fetching the data we need to write complex queries.
Read full review Scalability It helped us in getting from 0 to DSX without getting lost
Read full review Return on Investment Could instantly show data driven insights to drive 20% incremental revenue over existing results Still don't have a real use case for unstructured data like twitter feed Some of the insights around user actions have driven new projects to automate mundane tasks Read full review We have more users who can connect to the many different data sources. Our users do have existing SAS programming knowledge and that can carry over. Business functions are starting to rely on SAS Data Integration Studio work product shortly after introduction. Read full review ScreenShots