Chapter 5. The Internet of Things and Real-Time Data Analytics

Live data can have enormous value, but only if it can be processed as it streams in. Without the processing power required to ingest and analyze these streams in real time, however, organizations risk missing out on the opportunities in two ways: the applications will be limited to a relatively low volume and velocity of data, and the results will come too late to have real value.

This need for speed is particularly true for the Internet of Things (IoT). The IoT offers tremendous opportunities to derive actionable insights from connected devices, both stationary and mobile, and to make these devices operate more intelligently and, therefore, more effectively.

Even before the advent of the IoT, the need to analyze live data in real time, often coupled with data at rest, had become almost universal. Although some organizations have industry-specific sources of streaming data, nearly every organization has a data network, a website, inbound and outbound phone calls, heating and lighting controls, machine logs, a building security system, and other infrastructure—all of which continuously generates data that holds potential—and perishable—value.

Today, with the IoT, or as some pundits call it, the Internet of Everything, the number of devices streaming data is destined to proliferate to 30 billion or more by 2020, according to various estimates.

Only the GPU database has the processing power and other capabilities needed to take full advantage of the IoT. In particular, the ability to perform repeated, similar instructions in parallel across a massive number of small, efficient cores makes the GPU ideal for IoT applications. Because many “Things” generate both time- and location-dependent data, the GPU’s geospatial functionality enables support for even the most demanding IoT applications.

Figure 5-1. A GPU database is able to ingest, analyze, and act on streaming data in real time, making it ideal for IoT applications

For these and other reasons, Ovum declared GPU databases a breakout success story in its 2017 Trends to Watch based on the GPU’s ability to “push real-time streaming use cases to the front burner” for IoT use cases.

The ability to ingest, analyze, and act on streaming IoT data in real time makes the GPU database suitable for virtually any IoT use case. Even though these use cases vary substantially across different organizations in different industries, here are three examples that help demonstrate the power and potential of the GPU.

• Customer experience—GPU databases can ingest information about customers from a variety of sources, including their devices and online accounts, to monitor and analyze buying behavior in real time; this is particularly valuable for retailers with “Customer 360” applications that correlate data from point-of-sale systems, social media streams, weather forecasts, and other sources

• Supply-chain optimization—You can use GPU databases to provide real-time, location-based insights across the entire supply chain, including suppliers, distributors, logistics, transportation, warehouses, and retail locations, enabling businesses to better understand demand and manage supply

• Fleet management—Public sector agencies and businesses that own and operate vehicles can use GPU databases to integrate live data into their fleet management systems; IoT applications that track location in real time can benefit even more with the geospatial processing power of the GPU

The IoT era is here and growing relentlessly, and only a GPU database can enable organizations to take full advantage of the many possibilities. For those online analytical processing and other business intelligence (BI) applications that stand to benefit from IoT insights, some GPU-accelerated databases now support standards like SQL-92 and BI tools, as well as the high availability and robust security often required in such applications.