In any data management policy, there are two extremes: save everything (just in case), and delete everything that ages out. The two extremes work hand in hand, as eventually, you decide that even if you want to save it all, the realities of storage costs have forced you to delete your data arbitrarily.
Ideally, you would retain « interesting” data that might be useful and delete the rest. Even better would be to not collect the data in the first place.
Modern systems and applications span numerous architectures and technologies — they are also becoming increasingly more dynamic, distributed, and modular in nature. In order to support the availability and performance of their systems, IT operations and SRE teams need advanced monitoring capabilities. This Refcard reviews the four distinct levels of observability maturity, key functionality at each stage, and next steps organizations should take to enhance their monitoring practices.
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With microservices architecture becoming the de facto standard for web applications now, effective debugging and anomaly detection calls for a system that is observable — which means, the internal state of an application can be inferred by observing and tracking the metrics, traces, and logs.
Observability is all about data exposure and easy access to information required to find issues when the communications fail, internal events do not occur as expected or events occur when they shouldn’t. Here, you’ll learn and know about different microservices monitoring tools and how to monitor microservices. Let’s take a look!
If you’re paying attention to anything that’s happening in the development world, you’re likely familiar with the term “observability.” We’re seeing more and more monitoring companies from all different backgrounds jumping on the term to describe their solutions, many claiming their observability tool to be the factor that will take businesses to the next level.
Growing out-of-control system engineering, observability allows dev teams to unify and study the behaviors of various IT systems through the external outputs of the internal systems. In the case of software, that’s log events, distributed tracing, and time-series metrics. By unifying the data streaming through today’s complex IT environments, it certainly gives SREs and DevOps practitioners a leg up from traditional monitoring. But the data alone is no longer enough.
