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Bringing the Value of Real-Time Data to Mobile Apps

Andy_PiperIn this special guest feature, Dr. Andy Piper of Push Technology focuses on the data gymnastics required for coupling big data and mobile apps. Andy is CTO of Push Technology and is responsible for a team of highly skilled engineers ensuring that the platform delivers the step-changes in scalability, reliability and robustness that have become synonymous with Push Technology. He drives product innovation at Push Technology to continuously expand the capabilities of Diffusion™ cementing its place as a leader in the emerging and exciting market of efficient, scalable, real-time, high performance data distribution solutions. Previously, he was a Technical Director at Oracle with 18 years of experience working at the forefront of the technology industry.

Big data is accelerating, now doubling in size every two years. In fact, global data is expected to reach 44 zettabytes (that’s 44 trillion gigabytes!) by 2020. And, with new opportunities to apply data to real-time apps and services, big data is no longer simply growing in volume – it’s also growing in velocity.

Today’s data is constantly in motion, traveling in and out of databases, to and from connected devices, and across continents, all in less than the blink of an eye. This evolution of real-time data requirements represents a huge opportunity for app providers because it can power everything from critical health monitoring to in-game betting, as well as many new and growing use cases for the Internet of Things. In fact, the ability to properly manage real-time data will be a critical element for IoT success in general.

But, capturing the value of real-time data in a mobile app is proving to be quite a challenge for developers. Much like many companies first struggled to collect, transfer and store big data, they are now struggling to do the same for real-time data, primarily because the combination of speed and size is so difficult to ensure transit over a mobile connection. This is complicated by the fact that the Internet was designed to move documents, rather than data – so, it’s not surprising that the Internet is now struggling to deliver data at a velocity of gigabytes per hour! And yet, developers still must find ways to guarantee that data is sent and processed as quickly as possible to support apps that require real-time information.

One option to turbocharge app performance for real-time data is to reduce the amount of data that an app has to process. By shrinking the size of data packets sent to and from an app, developers can ensure their apps process data quickly and perform optimally, even over limited bandwidth, ultimately providing a positive user experience for real-time data services.

Take connected cars as an example. This emerging use case relies on data sent between sensors embedded in a car, to and from the driver’s device, and even to the manufacturer and other service providers. However, while all of those sensors could be constantly sending real-time data between end points, this data gymnastics is not always necessary. Drivers, manufacturers and other connected service providers don’t need to receive data about the oil levels, engine maintenance or brake pressure unless data has dramatically changed or the driver’s safety is at risk.

Developers can streamline these updates by implementing intelligent data distribution technology into their connected car sensors and apps. That way, sensors will send only the data deltas – that is, the data that has changed since the last update – eliminating all the extraneous data that has remained static. Similarly, location-based services that rely on real-time data will only relay changes in location, or may only send new data packets when a user has entered a specific geographic region, such as a retail store or museum.

Of course, just as we saw with big data, there will be many more challenges for companies to fully manage real-time data. They will not only need to sort out issues of storage, but connectivity, infrastructure and security are also likely to be key barriers to extracting the most value from today’s data. However, developers can already address the issue of speed when it comes to real-time data by sending smaller data packets to and from smartphones and other connected devices. With this head start, companies are sure to see significant returns on real-time data demands.

 

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