Research shows that 90% of ANZ IT professionals find observability crucial and strategic to their business. More companies have adopted cloud infrastructure and microservices to create their applications to deliver updates faster and be flexible around digital services.
However, these shifts in architecture and design impact highlight the value of agility and speed in deploying this tech. Observability takes data outputs from an app and then uses that data to understand how the app is performing. The standard definition for observability is that developers combine their app logs, metrics, and tracing to get a grip of app performance.
However, it is not as simple as just having data sets coming in.
How can you improve your observability?
Instead, it’s worth looking at how you can improve your use of observability data to achieve your goals. This can be more specific than simply gathering data over time, and then using it when something goes wrong. You can improve this process by setting up more specific goals based on continuously changing data from your applications and cloud infrastructure.
Getting your goals in place
The first area for observability is around app development and reliability. Using data from your apps’ continuous integration and continuous deployment (CI/CD) pipelines, you should be able to see how quickly you are rolling out updates and problems that come up over time.
This is still a reactive process. Instead, how can you use that data to spot potential problems, or ways to improve, before a problem arises? The big challenge for many app development teams and site reliability engineers is, paradoxically, not when something goes down.
After all, getting to the root cause here should be easier because the broken component should be obvious – for example, it could be DNS, a faulty change that companies should roll back, a cloud service that is not available, or a network connectivity problem.
Some of the hardest problems to deal with are not so black and white. Instead, services can degrade over time, still working but not at the level expected. To solve this involves some more preparations based on setting up the right metrics for each group of components.
Once these metrics are in place, you can use your observability data to track that performance over time and then be more proactive in fixing problems. A specialist approach to observability is around Kubernetes. More developers have turned to containers to host microservices applications, and Kubernetes is the de facto standard for managing them.
For observability, getting data out of your Kubernetes and containers will help show how those apps perform. This involves bringing data sources together, such as Prometheus, or using an open source framework like OpenTelemetry. With this data, one can connect any microservice performance errors to user experience and then make the right changes.
Getting a full picture
One area that is growing in importance for firms is how to track multi-cloud deployments.
Tracking multi-cloud deployments is another way companies can identify potential problems or ways to improve systems. Multi-cloud can mean different things to different organisations – from departments choosing their cloud providers to meet specific needs to firms planning their expansion across multiple markets with the most appropriate partner in a region.
Getting data from each cloud provider is only part of the story. Alongside this raw data, it’s essential to work on normalising this data and get it into one place for tracking.
While many cloud services are either compatible with each other or broadly similar in what they provide – for example, commodity services like block storage or compute – there will be some differences in how the cloud providers operate. There will be specific tools available that provide alternative services. Therefore, comparing information across cloud providers and getting consistent insight into performance is a task that observability data can support.
Similarly, app developers can get more value from their observability data. Rather than looking at this data solely for alerting or performance, continuous integration and continuous deployment pipeline data can be used to look for openings to optimise the whole process. Having tens or even hundreds of CI/CD pipelines in place is not uncommon for enterprises.
Consolidating data from those pipelines can then highlight the performance and benchmark data can be used to spot opportunities to improve performance at the team level.
Steering ahead confidently
No doubt, businesses must navigate the cyberworld with all its boundless opportunities and various unforeseen threats. Firms seek to collaborate and do business online with a strong seamless delivery of service and system performance backed by uncompromised security.
Observability data makes this possible through the provision of a complete picture and data consolidation. This can give all stakeholders the additional confidence needed to steer ahead confidently in an often uncertain and volatile world.
Sam Phelan is the Country Leader ANZ at Sumo Logic
