This is the second article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this segment, we’ll discuss the marriage of IoT analytics and the cloud. Previous parts to this special feature:
Marriage of IoT Analytics and the Cloud
The cloud is enabling innovation and driving the adoption of many new and powerful technologies, and IoT is no exception. One characteristic of many IoT applications is they generate “too much data.” You don’t necessarily know the value of that data and the process tends to be very elastic. The cloud, by its nature, is perfect for managing this kind of elasticity. The simple equation becomes IoT + Cloud = Big Data Killer App.
Predictive analytics and machine learning are very “spikey” jobs in terms of resource requirements. One day you wished you had 80 nodes, and then for the next 3 days you need none. One of the things that’s changed the equation for big data analytics and IoT analytics is having these elastic resources available. It has made it possible for start-ups, and large enterprises alike to do the data science and to have the compute resources and platforms.
There are two reasons why the cloud is driving IoT adoption. In a sense, the cloud is enabling innovation in IoT – in terms of data collection, and also data services and devices that can be exposed with an API. The APIs meet in the cloud. Further, as this market transitions to the mainstream, there is a need to simplify the process of unlocking big data insights. Cloud-based big data services coupled with IoT data stores are driving a lot of this, and increasingly making big data accessible in a simplified manner to the mainstream market. Big data cloud services are the behind-the-scenes magic of IoT. Expanding cloud services will not only catch sensor data but also feed it into big data analytics and machine learning algorithms to make use of it. Highly secure IoT cloud services will also help manufacturers create new products that safely take action on the analyzed data without human intervention.
Contributed by Daniel D. Gutierrez, Managing Editor of insideBIGDATA. In addition to being a tech journalist, Daniel also is a practicing data scientist, author, educator and sits on a number of advisory boards for various start-up companies.
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