In the world of IT operations, observability extends the principles of IT monitoring to successfully identify root causes of issues and resolve them promptly.
Cloud-native observability extends efficiencies by embedding together data from metrics, logs, traces, and events to empower operators, further extending them to the full gamut of multi-cloud hybrid IT.
Cloud-native observability is relevant for organizations that implement Kubernetes. As cloud-native computing represents a massive paradigm shift in enterprise IT. The observability part of the story reflects new ways of leveraging technology to manage the increasingly complex IT infrastructure.
The innovations follow three main themes, each representing an innovative aspect of the
the cloud-native paradigm shift that is transforming everything about how enterprises run
Real-time visibility into the root causes which accelerates the work of DevOps teams
Traditional monitoring tool vendors focus on the need of operators, giving them the dashboards that leverage sampled data, which may be minutes or even hours old. In contrast, some cloud-native observability tools allow near real-time visibility into incidents as well as their root causes.
Vendors flaunting real-time capability to cloud-native observability provide feedback for continuous integration and deployment or CI/CD activities, followed by the root-cause identification, analytics, and insight into the context of service dependencies.
Automated, AI-driven cause detection
AIOps – leveraging AI (especially, machine learning) uncovers anomalies in operational data to determine the root causes — is now a growing market in its own right. Multiple cloud-native observability vendors also offer AIOps capabilities, flaunting a cloud-native twist.
Such vendors are proactive since it continually assesses all significant factors to determine the best possible set of deployment choices and then automatically implementing them. It also recalculates on the go to maintain the top performance as different conditions change.
Cloud-native observability includes empowering different operators to fix issues, what better fix compared to the proactive optimization that prevents issues.
All the data processed real-time
Operational telemetry always encompasses big data – all the events, logs, and other streams of information coming off each application and infrastructure component continuously.
Historically, processing and storing vast quantities of information was cost-prohibitive, so the IT operations technologies had to struggle on samples. This includes small subsets of all data that is available statistically representing the behavior of the broader environment.
Today, the situation has gone worse as the number of data sources has exploded with the technologies and environments getting diversified in the modern IT landscape. Alongside the basic fact that much of the technology is dynamic as well as ephemeral, sampling becomes extremely impractical and ineffective.
Fortunately, the cost of processing and storing such data has also dropped, enabling some
cloud-native observability vendors to process available operational data, all the time, cost-
Making the right choice regarding cloud-native observability
The established vendors from this background have a broader, more complete offering as compared to the startups. The startups are also driving innovation in their concerned areas of focus.
More complete offerings make more sense generally, but they also get more difficult to leverage and implement to their full extent. The younger offerings may not have many features, but their time to value is usually quicker than the larger vendors.
As has always been true in the case of IT operations tooling, organizations can never have just one tool. Also, too many tools can also multiply issues, but the best-run shops leverage a particularly selected set of complementary tools. Many things are diverse about cloud-native computing, but the basic fact will remain true for the foreseeable future.