How to Create a Container Probe - Civo Academy
What are the basics of creating a container probe and how the process changes depending on the container probe used.
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How Sleuth measures Change Lead Time
Change Lead Time can be considered the most insightful of the four DORA metrics. But how do you measure it most accurately? In this video, Don Brown shows you how Sleuth measures Change Lead Time for code changes and how Sleuth breaks down that time into multiple buckets for the most detailed insight on what's slowing your team down. Check out these videos on how Sleuth measures other DORA metrics.
How Sleuth measures Change Failure Rate
Before you can measure the DORA metric for Change Failure Rate, you need to define what failure means. In this video, Sleuth's CTO Don Brown explains how Sleuth defines and measures Change Failure Rate, and how it ties failure back to deployments. Check out these videos on how Sleuth measures other DORA metrics: Give Sleuth a try and see why it's a deploy-based Accelerate / DORA metrics tracker both managers and developers love.
How Sleuth measures Mean Time to Recovery (MTTR)
The DORA metric Mean Time to Recovery (MTTR) tracks how long on average your failure spans are. In this video, Sleuth CTO Don Brown explains how Sleuth calculates this measurement, which gives you insight on how quickly your team can respond to and recover from failure. Check out these videos on how Sleuth measures other DORA metrics: Give Sleuth a try and see why it's a deploy-based Accelerate / DORA metrics tracker both managers and developers love.
No Longer Haunted by the Ghost of AIOps Past
How AIOps has evolved into an accessible and efficient solution.
Why a big bang approach is the wrong cloud strategy
Despite all the hype from the big cloud providers the truth is that most organisations rely on hybrid infrastructures now and will do so for the foreseeable future. Typically, this includes on-premises infrastructure and at least two public cloud providers. This is not a step on a journey to being 100 per cent cloud, it is the strategic destination many have chosen.
Autoscale your Kubernetes workloads with any Datadog metric
Editor’s note: This post was updated on August 9, 2022, to include a demonstration of how to enable highly available support for HPA. It was also updated on November 12, 2020, to include a demonstration of how to autoscale Kubernetes workloads based on custom Datadog queries using the new DatadogMetric CRD.
Monitoring Rails applications with Datadog
Rails is a Ruby framework for developing web applications. It favors the Model-View-Controller (MVC) architecture and includes generators that create the files needed for each MVC component. Rails applications consist of a database, an application server for running application code, and a web server for processing requests. Rails provides multiple integrations for its supporting database (e.g., MySQL and PostgreSQL) and web server (e.g., Apache and NGINX).
Why AIOps may be necessary for the future of engineering
Machine learning has crossed the chasm. In 2020, McKinsey found that out of 2,395 companies surveyed, 50% had an ongoing investment in machine learning. By 2030, machine learning is predicted to deliver around $13 trillion. Before long, a good understanding of machine learning (ML) will be a central requirement in any technical strategy. The question is — what role is artificial intelligence (AI) going to play in engineering?
Demystifying AIOps for IT practitioners
If your organization is looking to improve its IT service management (ITSM) and/or IT Operations Management (ITOM) capabilities, then it’s probably considering Artificial Intelligence for IT Operations, which is commonly called “AIOps.” But what is AIOps, and how will it help your IT organization’s IT management capabilities and, ultimately, business operations and outcomes? Let’s start with an AIOps definition.