Analytics @ The Edge allows users to make recommendations without moving all data back to the core of the IoT environment . In this design pattern , predictive models are created and sent to user devices . Security can be increased through the minimization of data movement . This design pattern limits the ability to add future features and functionality , but provides users an increased comfort level over their data control .
With either GCP analytics design pattern , IoT environments require two primary capabilities : flexibility and scalability . Flexibility comes from the ability to support a wide range of IoT data sources . Scalability provides the ability to seamlessly scale up and down as the number of IoT devices increases , and as the compute needs of the analytical engines change over time .
Compute Resource Platforms
Google Cloud offers two sets of capabilities to ensure applications have on-demand resources available to scale as needed :
Google App Engine
Google App Engine provides a Platform-as-a-Service for deployment of applications that are easily integrated with data resources and tools for data ingest orchestration . GAE enables users to quickly create applications to run in containerized environments , ensuring simplified migration and scalability .
Google Compute Engine
GCE is Infrastructure-as-a-Service for the deployment of virtual machines and compute resources and is commonly used as a scalable service to compute large , complex data sets within an IoT environment .
Google & IoT
GCE is commonly used as a scalable service to compute large , complex data sets within an IoT environment .
24 | THE DOPPLER | SPRING 2016