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Five examples where GPU databases are bringing ‘real-time’ to IoT analytics

If your organization manages large volumes of streaming IoT data, you’ll no doubt be familiar with some of the challenges of getting value and insight from these high volume, moving datasets.

Many companies have turned to Hadoop and other open source technologies to store and manage these IoT data feeds. This may typically involve “duct taping” a variety of tools together for a workable solution. Such methods are complex, take months to move to production, and may still process events in batches instead of in real time.

GPU databases bring revolutionary capabilities to IoT data and analytics. NVIDIA and Kinetica held a joint webinar this month about how the GPU database is opening new opportunities for the analysis of field-deployed devices such as autonomous cars, offshore oil rigs, cell towers, factories, and heavy machinery.

Are GPU Databases the Answer for IoT Analytics?

According to Ovum’s 2017 Trends to Watch, IoT use cases with GPU databases that “push real-time streaming use cases to the front burner” are this year’s breakout success story.

GPUs take traditional database operations and accelerate them by using thousands of small, efficient cores that are well-suited to performing repeated similar instructions in parallel.

Some of the latest GPUs feature over 4,000 cores versus just 16 to 32 cores on a typical CPU-based device. Additionally, GPU cores can crunch data far more efficiently and faster than CPUs, which process data sequentially.

These features make GPU databases ideal for analyzing massive datasets in real time, particularly for use cases where time and location matter.

Since most IoT data has both time and location data associated with it, additional geospatial functionality is also valuable for location-based analytics. Kinetica natively stores geospatial object types, and has a suite of geospatial functions and map-based visualization functionality built into it. These enable users to quickly view large moving datasets on maps and video, created at the time of query.

IoT Use Cases

There have been dozens of consumer and industrial IoT use cases deployed, with more to come. Some of the top use cases include:

  1. Fleet Management The proliferation of mobile networks has enabled rapid growth in fleet management systems. A prime example of how GPU databases can be used as a real-time analytics platform for fleet management is the United States Postal Service, which deploys Kinetica to optimize its business operations.USPS mail carriers use a device that scans packages and emits their exact geographic location every minute. With Kinetica, USPS can collect, process, and analyze over 200,000 messages per minute. It can analyze this breadcrumb data to understand where spending achieves the best results, make faster and more efficient strategic decisions, provide customers with more reliable service, and reduce costs by streamlining deliveries.Since Kinetica’s GPU database can be used to visualize geospatial data, dispatchers can efficiently plan territory assignments and make better use of routes.
  2. Smart Grid Energy and public utility companies are using “smart meters” to measure how energy resources are being used in homes and commercial buildings. These systems help utilities meet the demand for energy conservation, while also making billing easier for customers to understand.Homeowners who use smart meters have real-time visibility into their energy consumption and can adjust accordingly, while utilities are better able to meet consumer demand and balance production. In addition, smart meters can continuously monitor energy use, so utilities can react quickly to broken equipment or service interruptions.
  3. Manufacturing A key challenge facing manufacturing IoT is keeping up with the ingestion of streaming events coming from assembly lines, the supply chain, the manufacturing machines themselves, as well as the tags on individual items being produced.A GPU database provides the necessary performance requirements for today’s many manufacturing IoT use cases, including optimizing the entire manufacturing chain, performing streaming analytics on component functionality, tracking and monitoring inventory, materials and operations, detecting manufacturing defects, ensuring safety and avoiding failures, and tracking quality, returns, and warranty claims.
  4. Customer Experience GPU databases can be deployed at retailers to track and analyze huge volumes of moving assets and inventory in real time – ideal for generating faster and more relevant intelligence across a company’s supply chain.The classic IoT use case for retailers is customer 360: retailers can correlate data from point-of-sale systems, social media streams, weather forecasts, and wearable devices. This data is used to develop a better understanding of customers and the business by being able to query massive datasets in seconds vs. hours.
  5. Supply Chain Optimization GPU databases can be used to provide real-time, location-based insights across the entire supply chain, including suppliers, distributors, logistics, transportation, and retail locations for businesses to understand demand, manage supply, and track inventory in real time.

Delivering Speed, Scale, and Intelligence to IoT

GPUs enable Kinetica to deliver speed, scale, and intelligence for IoT by converging machine learning and online analytical processing. This enables real-time insights to get to decisions and actions faster.

Learn more about how a GPU database can accelerate IoT analytics, or contact us for a demo.

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