Bringing DevOps agility to the edge with APIs

Source – insidebigdata.com

Few question the value DevOps brings to organizations, particularly those that are forging ahead with digital transformations. Efficient build processes, quick release cycles and easy integration with partner features and functionality are exactly in line with the authentic experiences customers have come to expect. But the agile nature of DevOps is only beginning to be applied to IoT, enabling the computing of IoT device data closer to where it lives, at the edge, rather than in the cloud or in a data warehouse. And the value of this development is significant.

If the DevOps process provides organizations with streamlined efficiencies, IoT devices and their sensors that capture and store valuable data add a layer of relevancy. When done right, the information they provide can mean the difference between making the right offer at the right time and one that falls short of customer expectation. Edge computing takes this principle one step further with quick data processing closer to the device, or the source of the data. The challenge with this scenario, however, is how can established DevOps teams best facilitate data communication from IoT devices on the edge?

As valuable data is collected by IoT devices, pre-computing of that data is done at the edge. While this allows for faster, more efficient processing, the pre-decision making that is done there can be problematic for DevOps teams who are used to controlling the process from start to finish. To solve this problem, DevOps would likely look for a script to run, but of course, a successful script will largely depend upon architecture and other factors.

With edge computing, APIs are gaining a new, bigger audience with DevOps teams. As a connector between applications and IoT data, APIs traditionally enable data flow between devices and the cloud, from the cloud to your back-end systems and from users back to their devices. With edge computing, the data doesn’t necessarily travel to the cloud, but it still needs software development to interact with users. By their very nature, APIs are innovative and can be a great resource to help develop, test and deploy data solutions, but they can also create challenges for DevOps in those same areas as well as across implementation and versioning.

As DevOps face challenges in facilitating rapid communication with computing at the edge, there are a few best practices to consider:

1. Implement stringent testing processes. To ensure the tools you use are capable of managing the sprawl of IoT devices and their data sufficiently, testing is critical. APIs can help you manage this complex relationship by building different layers.
2. Be mindful of security – securing data in motion is not straightforward. And, the early stage technology used in IoT and edge computing means there will a learning curve for most people. Hone your security know-how and stay up-to-date on the security regulations that are also evolving.
3. Use controlled rollouts and define rollback procedures. While speed is a valuable attribute of DevOps’ release cycles, new technology can and will have challenges. There will be bumps along the way so consider building in controlled rollouts of any update, use gates and sequentially introduce the roll out to smaller percentages of users to ensure success before expanding to a wider group. Also, articulate your rollback procedures and implement kill switches. Lastly, make sure the entire process is transparent so the entire DevOps team can learn from the process and, in the event of a problem, ensure it doesn’t happen again.

Maximizing computing at the edge with API connectivity allows DevOps to deliver on its goal of rapid iterations with little inefficiency while also encouraging innovation. When done right, customers are happy and the business flourishes. It’s a win-win.