In the following post, we will explore the integration of several open-source software applications to build an IoT edge analytics stack, designed to operate on ARM-based edge nodes. We will use the stack to collect, analyze, and visualize IoT data without first shipping the data to the Cloud or other external systems.

GMT IoT Edge Analytics Stack architecture (Image by author)

The Edge

Edge computing is a fast-growing technology trend, which involves pushing compute capabilities to the edge. Wikipedia describes edge computing as a distributed computing paradigm that brings computation and data storage closer to the location needed to improve response times and save bandwidth. The term edge commonly refers to a compute node at the edge of a network (edge device), sitting in close proximity to the source a data and between that data source and external system such as the Cloud.

In his recent post, 3 Advantages (And 1 Disadvantage) Of Edge Computing, well-know futurist Bernard Marr argues reduced bandwidth requirements, reduced latency, and enhanced security and privacy as three primary advantages of edge computing. Due to techniques like data downsampling, Marr advises one potential disadvantage of edge computing is that important data could end up being overlooked and discarded in the quest to save bandwidth and reduce latency.

David Ricketts, Head of Marketing at Quiss Technology PLC, estimates in his post, Cloud and Edge Computing — The Stats You Need to Know for 2018, the global edge computing market is expected to reach USD 6.72 billion by 2022 at a compound annual growth rate of a whopping 35.4 percent. Realizing the market potential, many major Cloud providers, edge device manufacturers, and integrators are rapidly expanding their edge compute capabilities. AWS, for example, currently offers more than a dozen services in the edge computing category.

#edge-computing #time-series-database #iot #grafana #analytics