6 min read
Any Internet-of-Things (IoT) environment can be a data management challenge because of the huge volumes of data that are created and the latencies inherent in having global distribution. The challenges of aggregating data from consumer-oriented devices, like wearable technologies and smart thermostats, are fairly well understood. For those types of devices, the volume of data is due to the large number of devices, and each individual device doesn’t necessarily create much data. However, there are a new set of challenges for IoT “devices” that generate megabytes or gigabytes of data per second. Certainly, the infrastructure will have to change, as those volumes of data will likely overwhelm the available bandwidth for aggregating the data into a central repository. Vehicles, medical devices, and oil rigs are perfect examples of sources of data that need a much more powerful architecture than those needed by consumer-oriented devices.
Most IoT applications benefit greatly from having data and compute services close to the “things.” It keeps round-trip latencies and time-to-action low for an interactive experience (“act locally”). More importantly, it allows for filtering and summarization of data so that it can more practically be delivered to a central location for large-scale, aggregated analytics and machine learning (“learn globally”) with insights and models delivered back to the remote sites to be operationalized. Often times these remote sites have space constraints, so you can’t simply deploy a full suite of data management and analytics solutions.
This is where MapR Edge comes in. MapR Edge is a small footprint edition of the MapR Converged Data Platform that addresses the need to capture, process, and operationalize IoT data close to the source. It is designed to run on small computers such as mini PCs that are about the size of a small book. Despite the limited size, you still get a lot of computing power in these MapR Edge clusters, but also can take advantage of the power of your core MapR deployment as part of an overall IoT infrastructure. A MapR Edge cluster is between 3 to 5 nodes, requires a minimum of 16GB RAM on each node, and has restrictions on overall disk capacity.
Let’s look at some of the key features of MapR Edge:
Some examples on how customers are benefitting from MapR Edge include:
These are just a few examples of how MapR Edge can change the way IoT analytics are performed. If you have an environment where significant volumes of data are generated at remote sites, talk to us about how MapR Edge and the MapR Converged Data Platform can help.
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