Real-time applications are the key way in which digital transformation initiatives are being implemented. But these systems are notoriously hard to develop, deploy, and maintain, let alone make enterprise-grade.
By bringing edge computing to the development table, and making it a fundamental part of the application, these digital transformation solutions are better suited to handle the data loads and performance requirements necessary for enterprise-grade solutions.
So how do you effectively utilize edge computing to ensure that your real-time application is enterprise-grade? One way to answer this is through what I like to call the “-ilities” of real-time application development (scalability, availability, reliability, and agility). Let’s dive deeper into the “-ilities” and a few other important things to keep in mind to ensure your real-time application is up to the task.
In today’s fast-paced world, it’s expected that any modern system can easily and efficiently scale both horizontally and vertically. But there is another important dimension of scalability that is often overlooked, distributed scalability, meaning to have the ability to distribute your business logic to any number of nodes in the network. Where enterprises begin to struggle is when ensuring the efficient communication between endpoints in the system via command invocation, remote query, or another method.
So why is this third (and often overlooked) dimension of scale so important? When you have thousands or even hundreds of thousands of events per second across a number of distributed locations it is essential to process the data closer to where it lives. This approach reduces pressure on cloud networks, provides lower latency for modern systems that require extremely fast response times, and ensures your application can continue to run even when the wide-area network is down.
One major problem enterprises face when building real-time applications in a non-distributed fashion (having edge locations all pointing back to a single cloud endpoint) is if the connection to the cloud goes down, then your application is no longer available. In a distributed architecture, since all the business logic can run at each one of those individual edge nodes, there’s no single point of failure in the system. In this case when the cloud connection goes down the edge devices continue to run, either sending data to an alternate location or storing it to send when connection returns.
This also comes into effect when updating applications or rolling out new logic to edge devices in the system. If each node is not relying on a pure central location, then specific maintenance windows can be assigned to transfer compute power to other devices on the network while an update is made, ultimately keeping the application online.
Having agility in your real-time application allows you to quickly and efficiently meet business mission-critical requirements and respond to changes in the environment as soon as they occur. This is referred to as development agility and is the most commonly referred to type of application agility.
Another important facet of agility in real-time systems is deployment agility, which is broken down into dynamic deployment (being able to deploy new applications or updates to existing ones with no downtime) and asymmetric deployment (gradually deploying new features to the application in sequential steps).
A real-world example of this would be a smart city with 100 IoT-enabled traffic cameras. Dynamic deployment would be deploying a new AI algorithm trained to search for specific car make and models to all cameras in the network in the case of an amber alert. Asymmetric deployment would be updating five out of the one hundred cameras to test the new features before rolling it out to the entire network.
By developing your application on a platform that provides both development and deployment agility you can ensure your business is prepared for any future changes or additions that need to be made to the real-time system and that they are carried out in an organized and timely manner.
It is expected that data in-flight and data at rest will be secure in any modern application; but steps can be taken to limit the attack surface and limit unnecessary data aggregation. In this age of increased data scrutiny and privacy concerns, you must ensure that the right permissions and visibility of assets is only available to the subscribers that require access.
Edge computing allows you to limit how the data is transferred and what kind of data is being tracked across the network by filtering sensitive information on the edge device.
The Platform Approach
When you embark on the journey of putting together an enterprise-grade real-time event-driven application you will quickly realize there is both a level of developmental complexity (in all the different tools that you might need to use), deployment complexity (getting application logic into the environment), and operational complexity (keeping all these different services running and connected). This involves managing many different layers in your application stack including a messaging layer, filtering layer, aggregation layer, stateful layer, stream analytics layer, and situational layer just to name a few.
As you can see this is no small feat and is the reason why so many organizations try and fail to make the switch to real time. In order to successfully develop, deploy, and maintain an enterprise-grade real-time application it is paramount to utilize a platform that brings the power of edge computing into one integrated real-time application development environment.