Statistica™ integrates fully with Spotfire, TIBCO's well-known and popular visualization front-end.
Statistica™ workspaces be used by Spotfire as data functions. This allows Statistica™ to be a no code analysis engine for Spotfire users.
You can use Spotfire data connections from within Statistica™. This means that dozens of data sources are now immediately accessible. Load data from Amazon, Cloudera, Hortonworks, Salesforce, SAP HANA, TeraData and many more directly into your workspaces.
The R integration has been available in Statistica™ for many years and offers you a wide range of applications:
You can use R in different ways in Statistica™: On the desktop, centrally managed and executed using the server as a script and integrated into workspace nodes.
A pharmaceutical customer is using the Statistica™ server and R integration to monitor the production of pharmaceuticals. The customer benefits from the fact that data sources can be easily managed, and validated systems can be built in Statistica™. The customer combines this with the advanced capabilities of R to build complex forecasting models. With this system many thousands of parameters are compared and monitored. The results are summarized in reports for the departments and regulatory authorities.
In the financial sector, a customer uses the Statistica™ server and R integration to quickly and easily obtain analytical workflows for credit risk forecasting. Statistica™'s simple user interface and the ability to use the Statistica™ server to manage and provide R components facilitate the customer's work. At the same time, the effort required to manage the R components is reduced.
The Python programming language can be used in Statistica™ nodes and thus can be integrated into analytical processes. Python is supported in versions 2 and 3, and Statistica™ also comes with its own IronPython engine which is included with every installation of Statistica™. Python has evolved from an elegant programming language to an analytical platform with many functions, especially in the area of deep learning, and thus offers a useful supplement to Statistica™. Python also comes in handy to connect Statistica™ to a multitude of modern APIs as these are often available through python libraries.
C# is a powerful programming language especially used in application development, but its analytical capabilities are not as extensive as those of R and Python.
Instead C# offers access to the many .Net libraries and can be used for easy interaction with the Windows environment.
The integration of Spark Scala differs from other integrations in that it is rather a remote control for Spark. This is a sensible approach since Spark is an environment to perform big data analysis in a cluster with data sets being (usually) too large to be handled by a traditional computer.
A common scenario to benefit from the combination of Statistica™ and Spark is to process the data in the cluster using Spark, reduce it to a manageable size and then import it to Statistica™ to apply complex analytics on it.