In recent years, the number of smart, connected devices making up the Internet of Things (IoT) has grown explosively. In 2009, when Cisco estimates the IoT was born, people were just beginning to play with microcontrollers to build smart devices. Since then, the number of devices has grown rapidly, and the IoT has expanded to include not just traditional computer-based devices and electronic appliances, but also wearables — such as watches, clothing, and rings — and vehicles, from the smart, self-driving cars currently in development to drones and other flying things.
By 2020, Gartner expects the IoT to have over 20 billion connected things. With that many connected devices transmitting information, there will be an enormous amount of processing to be done. To cope with this rapid expansion in the Internet of Things, successful IoT platforms will need a data architecture that can address significant challenges in terms of speed, scalability, variable workloads, and other issues. What type of data architecture can handle these challenges? Before discussing the technology needed to tackle all of the issues about IoT, let’s take a closer look at some popular IoT use cases.