Current observability practice is largely based on manual instrumentation, which requires adding code in relevant points in the user’s business logic code to generate telemetry data. This can become quite burdensome and create a barrier to entry for many wishing to implement observability in their environment. This is especially true in Kubernetes environments and microservices architecture.
Modern software services are expected to be highly available, and running a service with minimal interruptions requires a certain amount of reliability-focused engineering work. At the same time, teams also need to build new features and improve existing ones, so that users are delighted and don’t churn.
The rise of public cloud services has enabled businesses to innovate faster, scale effortlessly, and adopt more advanced technologies easier than ever before. However, there’s a dark side to using public cloud services: complexity and cost. Public cloud services can scale to handle almost any workload, but in doing so, they can quickly generate unpredictable costs for your business.
Customers have had a lot to say about the new Splunk Observability Cloud since we announced general availability on May 5, 2021. For the first time ever, IT and DevOps teams can get all their data in one place with unified metrics, traces and logs — collected in real time, without sampling and at any scale. What makes Splunk Observability Cloud unique from other solutions? We’ll let our customers do the talking.
You might have noticed some visual changes happening in Honeycomb lately. Colors, typography, icons, and some features have started to look a bit different. While these changes are just beginning to make their way into the product, we’ve been working on them for some time. Let’s look at what has been going on behind the scenes to make them happen.
When you’re operating a web application, the last thing you want to hear is “the site is down." Regardless of the reason, the fact that it is down is enough to cause anyone responsible for an app to break out into a sweat. As soon as you become aware of an issue, a clock starts ticking — literally, in some cases — to get the issue fixed. Minimizing this time between an issue occurring and its resolution is arguably the number one goal for any operations team.
An observability solution should help any incident responder understand what changed and why. A lot has been written on the difference between monitoring and observability, but an easy way to understand how both are integral to incident response is to consider how customers use PagerDuty—with both monitoring and observability tools—to get to the right answer.
But it’s better than nothing… Most of the industry is racing to adopt better observability practices, and they’re discovering lots of power in being able to see and measure what their systems are doing. High data availability is better than none, so for the time being, what we get is often impressive. There’s a qualitative difference between observability and data availability, and this post aims to highlight it and orient how we structure our telemetry.
Today, we released our second annual Observability Maturity Community Research Findings report. This year-over-year report identifies trends occurring in the observability community that we use to further develop our Observability Maturity Model. Our goal in running this annual report is to understand community perceptions and awareness of observability, how engineering teams are approaching observability, and mapping an observability maturity model that reflects current research findings.
