and real-time analytical capabilities . These are a commodity ; they should be leveraged using open APIs to allow for easy integration with external applications , and thus shouldn ’ t be dependent on the databases .
In this architecture , there ’ s little reliance on proprietary database features , such as stored procedures and triggers . The databases can reside on customers ’ premises or in a private or public cloud . IoT data comes from sensors and devices , where the data can be gathered and processed in real time . An example is outdoor temperature data that needs the data response layer to make core decisions on the information from the thermometer ( an IoT device ) without having to send the data back to the cloud .
An RDA uses special-purpose databases that can provide unique capabilities , such as in-memory data service for ultra-high-performance IoT systems . This is for long-term analytical workloads , and a variety of database components can exist in the model . Data persistence is mandatory . The OLTP database , regardless of the chosen technology , is logically coupled to the data residing at the network edge through abstraction .
Physical Databases
OLTP |
Analytical |
External |
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Device |
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Data |
Special Purpose
Figure 2 : RDA ’ s physical database layer provides high performance storage for IoT systems
Virtual Databases
Logical Data Binding
Physical Databases
OLTP |
Analytical |
External |
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Device |
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Data |
Special Purpose
Figure 3 : The virtual database layer provides the ability to change the database structure , without forces changes to the physical databases
48 | THE DOPPLER | FALL 2016