This is the second in a series of blog posts exploring the role that intelligent observability plays in the day-to-day life of smart teams. In this post, meet our clever ITOps engineer, James, as he reduces noise and distraction using intelligent observability.
Nobody will dispute that a common goal of DevOps pros and SREs, and really any company today, is to delight their customers more by disappointing them less. This was the theme of a recent live webinar focused on announcing a new game-changing partnership between Datadog and Moogsoft. The live session combined remarks by Moogsoft CEO Phil Tee and CTO Dave Casper on bringing together the best of these two technologies with a new seamless integration.
Thursday morning, and I’ve done some yoga, a ten-minute meditation and am at my desk in my hastily thrown up garden office with a mug of green tea by 08:30am. I’m really not missing the commute to our old HQ (now permanently closed, thanks to the pandemic) in the heart of Seattle and am enjoying an extra few minutes in bed and getting mindful before logging in.
Context is king, they say, and anything you can do to improve context both makes decisions and assessments more reliable and speeds up the decision process. A new, bi-directional integration between Moogsoft Observability Cloud and Datadog does just that. Many SRE teams rely on Datadog to provide comprehensive information about their application stacks.
The shift to Observability Over the last six months, unified monitoring, log management, and event management vendors have reoriented their technology portfolios (often without any change to the underlying functionality) towards Observability. In so doing, a fair amount of confusion has been generated in the market.
As we look ahead to 2021 and beyond, we can expect no shortage of opportunities to re-think IT for this era where digital infrastructure is critical to the survival of every business.
There’s an AI-led developer and operations (DevOps) evolution afoot which is stoking SREs’ increasingly critical efforts to assure and improve the customer experience by automating the toil out of observability. This movement feeds on a supercharged process of turning telemetry into actionable insight by automatically drawing anomalies, changes and events out of the full-stack event and telemetry data, and analyzing it for correlation and causality.
DevOps practices, and the teams that implement them, are becoming increasingly critical to the value which any company provides its customers. This was the key message throughout a recent fireside chat between DevOps Institute Chief Ambassador Helen Beal and Moogsoft VP of Product and Design Adam Frank. A great paradox of the digital era is that, once written, software is invisible to those who write it.
Under the hood, Moogsoft Observability Cloud extends AI-based intelligence so that it starts with raw observability data analysis. It discovers your infrastructure services to collect and analyze the time-series metrics locally, along with turning time-series metrics and event data from your existing tools into actionable insights.
Legacy monitoring tools fall short for SRE teams and DevOps pros tasked with maintaining uptime of key applications in modern, cloud-based IT systems. To have visibility and control over these environments, these teams must collect and analyze more granular, underlying system information — observability data. This article explains why the only way for SRE teams and DevOps pros to extract the necessary insights from this data is through the application of AI capabilities.
