Log Analytics for CloudOps
The cross-functional discipline of Cloud Operations (CloudOps) applies ITOps and DevOps methodologies to the management of cloud applications and infrastructure. Executed well, CloudOps helps enterprises maintain stable and yet agile cloud environments. But this requires effective analytics of logs—those tiny files that capture events such as user actions, application tasks, and compute errors, as well as the messages that applications and cloud components send to one another. CloudOps teams that analyze their logs effectively can achieve stability by optimizing performance, controlling costs, and governing data usage. They can stay agile by responding to events that require speed, scale, or innovation.
But the challenge of log analytics for CloudOps boils down to a simple analogy: you need to see the forest through many, many trees. To make sense of all the trees—including trees that remain on premises—you need to ingest, transform, index, and store millions of logs at high scale and low latency. This generates processing overhead that can choke pipelines or create sky-high compute costs. To overcome this challenge, you must either throttle your logs or widen and extend your analytics pipeline to process more logs. New indexing tools help widen your analytics pipeline, making CloudOps more effective.
CloudOps teams should identify and remediate bottlenecks in their log analytics pipelines, starting with those that cause the most business pain. They should decide whether to reduce the logs or widen the pipeline, based on their staff expertise and available tools. Finally, they should establish common methodologies and tools among CloudOps engineers, DevOps engineers, developers, site reliability engineers, and IT managers.