In response to the emergence of distributed and virtualized hybrid infrastructures, monitoring tools have become progressively more sophisticated. Their feature sets offer capabilities unheard of just a decade ago, delivering real-time visibility, better analytics, and the ability to aggregate data from groupings of servers, applications, devices, etc.
It’s a logical progression to consider the benefits of integrating network and systems data already available for existing management and monitoring solutions with observability. Converting from individual module ownership or moving away from using different vendors for your monitoring to a single full-stack solution, like SolarWinds Hybrid Cloud Observability, could help your teams more rapidly conduct root-cause analysis and increase issue resolution.
Find It and Fix It Faster
In discussing observability, two words are increasingly joined at the hip: “visibility” and, well, “observability.” Though this makes perfect sense (after all, the whole point of observability is to take monitoring to a new level by delivering insights about why events are happening as opposed to mostly what is happening), there are subtle differences between the two.
Due to their nature, hybrid cloud infrastructures are distributed and dynamic, which poses visibility challenges at every turn. It’s no wonder simply adding more monitoring tools from separate vendors can create additional data volume with increasingly less context. When an outage or performance issue arises, the unfortunate blame game that often ensues can be avoided by having a single version of truth available for review. Adding to the confusion, the lack of automation in monitoring tools can result in an overreliance on error-prone manual tasks and unfocused remediation efforts.
Analyzing torrents of alerts and the ongoing deluge of log data produced by traditional monitoring takes time. Troubleshooting based on partial visibility is inefficient and often leads to finger-pointing. Without the benefit of machine learning and automation to gain actionable intelligence, valuable hours can pass until remediation efforts can begin. On the other hand, Hybrid Cloud Observability’s ability to cut through massive volumes of historic metrics, logs, and trace data helps deliver visibility buttressed by a mosaic of external outputs sufficient to provide an accurate assessment of the internal state of the infrastructure in question.
By comparing otherwise disparate data types side by side, it becomes possible to correlate multiple entities on a common timeline and even move back in time to analyze the exact conditions in place when the first alerts occurred. Using a drag-and-drop function for choosing performance metrics from multiple sources and data types within Hybrid Cloud Observability, these concurrent events can be overlaid on a single chart. Sharing this common view with admins across the IT team fosters collaboration and a coordinated effort to resolve the issue.
Screen grabs of Hybrid Cloud Observability performance analysis (PerfStack™) dashboard illustrate the advanced visibility the solution provides to accelerate mean time to resolution (MTTR):
Drag and drop multiple data sources from the metric palette to help isolate root cause. PerfStack™ automatically overlays your data on a common timeline for immediate visual correlation.
In considering an observability solution, the “must-have” list is short but critical: full-stack visibility leveraging automation and machine learning in support of delivering actionable intelligence and cross-domain control. Hybrid Cloud Observability satisfies these needs with minimal configuration effort and surprisingly fast and seamless deployment.