Simplifying Development and Production of a Real-Time Applications (RTA) With Data Analytics

Life happens in real time. Unfortunately, many software programs and applications don't. It is so essential for enterprises to be able to bridge the gap between what is happening and what their systems are reporting when network security and system functions are on the line.

There is a big push toward using real-time data analytics in the development and production of real-time applications (RTA). This is a positive step because the only way to properly develop something that operates in real time is to measure it with technology that also operates in real time. The result is that end users will experience flawless, streamlined operations. Users on the development end will also experience a smooth process that measures activity and provides clear reports.

What Is an RTA? 

An RTA is an application program that appears to function in real time. While an RTA may appear to be functioning in precise real time to users, there is actually a latency of a few seconds or less. RTAs are most commonly used in e-commerce transactions, online chat services, streaming entertainment channels, call centers, online gaming, online storage spots and many other regularly used exchanges. Of course, the stakes are extremely high while developing RTA’s such as those that will be used by banks and financial institutions to make real-time financial transactions. There are some strict guidelines that an application must fit to be considered an RTA. Whether an application can qualify as an RTA typically comes down to the measure of its worst-case execution time.

Why Worst-Case Execution Time (WCET) Matters 

An RTA is only as good as its slowest response time. Worst-case execution time (WCET) is the maximum length of time a computational task could take to execute on a specific hardware platform. Being able to accurately measure WCET is important because most RTAs operate on platforms where they are part of a bigger picture. The bottom line is that real-time systems have non-negotiable deadlines to be considered effective. There can simply be no wild cards when it comes to latency. This is why implementing and utilizing a system that handles stream processing on a large scale is crucial during both the development and deployment of an RTA.

How Analytics Can Improve the Performance of RTAs

You can’t develop something to operate in real time unless you can test its effectiveness and performance in real time. Therefore it’s so important to use real-time data analytics when developing and testing RTAs. Data is really all developers and software engineers have to go by once all or portions of an RTA have been deployed. Data is useful during the development phase of an application because it can measure, predict, and adjust. The truly great thing about real-time data applications is that they provide so many different layers of reporting. For instance, many data programs provide real-time visualizations that break down things like activity, response times, latency, and other key indicators. This is quite valuable for decision makers and developers. Real-time data analytics deliver management and monitoring tools that make it possible to know exactly how an application is performing both in real time and over long periods of time.

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