Digital transformation projects have been the driving force behind many technology investments in recent years, with these initiatives essential to the modernisation of technology environments and the shift towards more widespread adoption of cloud native technologies, such as open source container orchestration system, Kubernetes.
Countless organisations see the value of these projects, thanks to their positive impact on developer productivity and engineering velocity, allowing a greater focus on delivering excellent user experiences and innovative features. In spite of this, digital transformation initiatives can expose unexpected challenges in operating complex systems, where data silos exist between applications, orchestration, and infrastructure. In order to remove these silos, engineering teams must take steps to future-proof their digital transformation projects.
Use real-time data
A doctor wouldn’t rely solely on historical information to diagnose a sick patient with new symptoms, and the same goes for engineers troubleshooting problems in their tech stack. Having access to real-time telemetry data is essential to understanding system health and enables digital transformation projects to be executed much more seamlessly.
Issues can be swiftly addressed by replacing the practice of manually sampling historical data. The key benefit to having real-time, accurate data is faster detection and response time, which means that issues can be more quickly detected and resolved, and more time can be spent on projects that have a direct impact on revenue.
Painting a complete picture of real-time telemetry data also helps to remove information silos and creates a more unified and accurate view of all systems and background issues. It also removes the need to toggle between tools, which is time-consuming and often prohibitively expensive. This approach ultimately allows engineers to switch focus from debugging to innovating.
Eliminate data silos with a single platform view
The future of software stacks often involves seamless integration between services, but many engineers are still swivelling between screens and using multiple tools to ascertain the health of their environments. In many cases, when issues emerge, they end up bouncing tickets between different internal teams to identify the root cause, which can delay the resolution of issues and cause end users to become frustrated and disengaged.
Applications rely on the underlying infrastructure being healthy, performant and scalable, and the addition of orchestration systems like Kubernetes adds another layer that needs to be monitored. Using multiple tool sets to monitor each layer of a software environment has the potential to create data silos, and these silos prevent engineering teams from truly understanding how symptoms in the various layers of their stack relate to each other.
Having a single view of infrastructure, applications, and end-user experience in one place makes it much easier to see where the root cause of an issue is and who is responsible for resolving it. Observability platforms that integrate all elements of the software stack into one view enable engineers to quickly see the status and count of hosts, containers, services, events, logs and alert activity in one location. These elements are crucial to overall system health, and this approach allows infrastructure-related performance issues to be diagnosed much more easily.
Improve the bottom line by adopting modern observability pricing
Many infrastructure monitoring tools’ pricing models were built before the public cloud and auto-scaling, meaning that they are inflexible and poorly suited to modern technology environments where the number of hosts naturally varies to match demand. This has made monitoring costs unpredictable and inefficient for many organisations, but by shifting to a more modern pricing model, engineers can build efficiency and sustainability into their digital transformation projects as well as remove duplicate costs.
As companies of all sizes tighten their purse strings, digital transformation projects may be the last thing on the minds of some technology leaders. But even in challenging times, startups and scaleups continue to mount challenges to the status quo, which is why continued innovation from engineering teams is vital to all companies looking to take or retain market share.