The GDAPI solves the industry-wide problem of exchanging electronic information between disparate organizations discussing different aspects of the same underlying reality. The standard and supporting SDK are freely available to the industry for implementation.


To provide the industry with a standard means of exchanging gaming data electronically.


The gaming industry relies on several different broad classes of organization, each of which must exchange data with all the others. Though each such exchange includes its own unique "take" on the data, all of these exchanges relate to the same underlying reality. Manufacturers must track which hardware and software can be installed in which cabinets, must submit packages of hardware and software to testing labs, and must fulfill customer orders while abiding by all the pertinent regulations and permitting processes. At each of these stages, different pieces of information relating to the cabinets and games in question are required.

Testing labs must accept submission packages, must track the testing process through each of its stages, must generate reports per submission and jurisdiction, and must publish these reports to the appropriate regulators in those jurisdictions. At each of these stages, different pieces of information relating to the tested hardware and software are required.
Gaming regulators must download and track the results of the labs' testing, must keep the operators in their jurisdictions apprised of which hardware and software is approved for use, must review and/or approve changes to the operators' gaming floors, and must conduct periodic audits of the operators in their jurisdictions. At each of these stages, different pieces of information relating to the games in their jurisdictions are required.

Casino operators must order and process gaming machines, must install and maintain those assets throughout their life cycle on the gaming floor, must ensure and report on the compliance of their operations, and must follow proper procedure to end-of-life their gaming machines when necessary. At each of these stages, different pieces of information relating to the machines on their floors are required.


The GDAPI is intended to comprise a comprehensive model of gaming data which both reflects the underlying reality in which all gaming organizations operate, and accommodates the various perspectives on that information each specific gaming organization requires. As such, it is designed to be modular, extensible, and descriptive.

 Click here to download the SDK

    Click here to view the Data Model

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     Click here to view Version History


Very few organizations need to track the entire model: testing labs, for example, are generally insulated from tracking a specific cabinet’s individual location in a bank of machines on the gaming floor of a particular casino. Similarly, any given operator has no need to know the details of jurisdictions in which he or she does not do business. As such, the GDAPI supports incorporating only as much of its overall model as necessary to provide the functionality required by it of a particular user.


While the GDAPI is designed to model an underlying common reality, any given entity may have unique requirements for handling additional pieces of information pertinent only to it. The structure of the GDAPI is such that it can accommodate such additions while maintaining consistency and compatibility with other consumers of the information who do not need that additional information. Moreover, the GDAPI explicitly supports submission of extensions back to the GDAPI maintaining authority, such that they can be folded into subsequent versions of the model.


Though there is a common reality about gaming that all participants work within, each gaming organization has historically created its own unique model of that reality. In order to accommodate all comers, the GDAPI model is designed to respect those individual models, while also providing a framework to translate information from one model to another. That is, it does not attempt to define the “right way” to model the data – instead, it defines methods to take data from an existing model and present it such that it can be translated to other models.

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