Jump to content
  • Analytic Development Languages Supported by Data Science


    TIBCO provides data science for everyone: Data Scientists, Data Engineers, Developers, and so on. However, each of these roles has different preferences and requirements for how to do data science, including the choice of the development environment and the coding languages. Fortunately, TIBCO Data Science (a single, unified platform for creating and operationalizing data science) can handle most preferences, technologies, and languages. Here is a list that organizes them, with links to tips & tricks elsewhere in the Community to help you.

    Development Scripting Languages and Custom Extensions

    R, Python, Scala, Java, C#, C, SQL, MDX, Pig, Hive QL, Spark

    TIBCO Data Science tools offer a wide range of native features and possibilities where a user does not need coding at all. Nevertheless, in addition to no code options there is  also possibility to use other scripting languages for implementing data science computations. TIBCO Data Science tools typically use nodes/operators (basic elements from which the analytical process is built) as part of their graphical workflows where a user can incorporate code from various scripting languages. During the runtime, such node/operator executes the code according to the type of code (e.g. call another execution environment) and typically brings back the results which can be utilized in further analysis by consequent nodes/operators. An example of such a workflow is shown below.

    scripting_nodes_0.png.2e7fa04b206432c6642a203510cf84ff.png

    Deployment (Code Generation Languages)

    Predictive models generated in C, C++, C#, Java, PMML, PFA, SAS, SQL Stored Procedure in C#, SQL User Defined Function in C#, Statistica Visual Basic

    Once the predictive modeling is done, tools can produce a code of the model which can be afterward used for further scoring  in TIBCO Data Science platform itself or in other applications (deployment environments, real-time scoring engines, or even gateways).  

    Execution Environments

    TIBCO Data Science tools can use and invoke as part of the computational process following environments:

    TERR, R, Python, SAS, MatLab, most RDBMS, most flavors of Hadoop, Hive, Spark, Flogo

    Analytic Market Places

    Azure ML, AWS, Apervita, Algorithmia, H20, Microsoft CNTK, TIBCO Community Exchange

    External models, methods, and know-how can be also taken from external sources like marketplaces. Again, you can use nodes in your analytical workflow to invoke and use information from an external source. Such nodes can be a model, a single method, or an entire analytical procedure. All of this is combined inside a single processing environment of TIBCO Data Science.

    References

    You can find some of the references connected with the topic below: 

    Using development scripting languages directly from TIBCO Spotfire:


    User Feedback

    Recommended Comments

    There are no comments to display.


×
×
  • Create New...