Operations | Monitoring | ITSM | DevOps | Cloud

In one of my previous blogs I explained how important it is for a modern observability platform to provide “the observers” full, flexible access to all raw telemetry. Observability’s promise to find unknown unknowns relied directly on the ability of fast, powerful and multidimensional high-cardinality analysis of raw data, to uncover previously unknown patterns that have not yet been visualized as a metric, dashboard panel or an alert or anomaly event.

Running and troubleshooting production services requires deep visibility into your applications and infrastructure. While basic logs and metrics are available out of the box with Google Cloud Compute Engine (GCE), capturing advanced data used to require the installation of both a metrics agent and a logging agent.

The growth of technology has led to more efficient and relevant digital experiences, and customers continue to expect more out of those interactions. That’s true no matter their location and no matter which device they choose to use. Companies that cannot provide these kinds of personalized interactions for their customers find themselves falling behind the competition as technology continues to advance.

Slack experienced meteoric growth between 2017 and 2020—but that level of growth came with growing pains. In his talk at the 2021 o11ycon+hnycon, Frank Chen (LinkedIn), a Slack Senior Staff Engineer, detailed one of Slack’s biggest pain points in that period: flaky tests. A flaky test returns both a passing and failing result despite no changes in the code. At one point, between 2017 and 2020, Slack’s flaky test rate reached as high as 50%.