At in San Jose, Calif., Tuesday, took the wraps off a new small footprint edition of its Converged Data Platform geared for capturing, processing and analyzing data from internet of things (IoT) devices at the edge.
MapR Edge, designed to work in conjunction with the core MapR Converged Enterprise Edition, provides local processing, aggregation of insights at the core and the ability to then push intelligence back to the edge.
“You can think of it as a mini-cluster that’s close to the source and can do analytics where the data resides, but then send data back to the core,” says Dale Kim, senior director, Industry Solution, at MapR Technologies.
“The use cases for IoT continue to grow, and in many situations, the volume of data generated at the edge requires bandwidth levels that overwhelm the available resources,” Jason Stamper, analyst, Data Platforms & Analytics, 451 Research, added in a statement. “MapR is pushing the computation and analysis of IoT data close to the sources, allowing more efficient and faster decision-making locally, while also allowing subsets of the data to be reliably transported to a central analytics deployment.
Many core IoT use cases, like vehicles and oil rigs, operate in conditions with limited connectivity, making sending massive streams of data back to a central analytics core impractical. The idea behind MapR Edge is to capture and process most of that data at the edge, where the data is created, then send summarized data back to the core, which then aggregates that summarized data from hundreds or thousands of edge IoT devices.
MapR Technologies calls this concept “Act Locally, Learn Globally,” which means that IoT applications leverage local data from numerous sources for constructing machine learning or deep learning models with global knowledge. These models are then deployed to the edge to enable real-time decisions based on local events.
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