Operations | Monitoring | ITSM | DevOps | Cloud

Sending custom metrics to Datadog allows you to monitor important data specific to your business and applications, such as latency, dollars per customer, items bought, or trips taken. And tags are key to being able to slice and dice these custom metrics to quickly find the information you need. But collecting enough custom metrics to have complete visibility can be cost prohibitive. For example, you might run microservices instrumented across thousands of containers.

To explain how cost anomaly detection works in AWS, let’s first look at an analogy. Imagine you strategically propagate, cultivate, harvest, and replenish trees for a thriving forest products company. In keeping with your eco-friendly policy, you only harvest the trees with straight and tall trunks. You leave irregularly shaped trees alone. Your company doesn’t harvest trees you won't use.

Software development is getting faster and more complex – frustrating IT operations teams more than ever. So, DevOps gained popularity in order to combat siloed workflows, decreased collaboration and a lack of visibility. While establishing a culture of DevOps has helped teams collaborate better and deliver reliable software faster, DevOps teams don’t necessarily have someone specifically dedicated to developing systems that increase site reliability and performance.

Microsoft Azure is the public cloud computing platform by Microsoft which offers software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS). It comprises more than 600 cloud services and supports varied operating systems, databases, and developer tools. And, even better, Splunk On-Call integrates with Microsoft Azure to help on-call teams improve incident response for Azure-based environments.

DevOps-oriented engineers live at the intersection of IT operations and software development – understanding much of what it takes to maintain IT infrastructure while also being able to write code and deploy new services. DevOps-minded teams not only create services – but they also maintain them. A DevOps structure forces teams to take accountability for their applications and infrastructure instead of allowing developers to throw code over the proverbial wall to IT operations.

Continuous integration (CI) makes the cycle from design to code to building artifacts seamless and consistent. Continuous delivery (CD) makes delivery of that artifact to an environment the same every time. But, what about the actual environment the artifact is running in? Is it the same every time? That’s a hard thing to guarantee — unless you take advantage of an Infrastructure-as-Code (IaC) approach. This post explains how to use Infrastructure-as-Code to improve CI/CD.

Because DevOps practices can bring great speed and reliability to the software delivery lifecycle, release management can seem daunting. But, the improved visibility and collaboration brought about by DevOps can also help with the release management process. While the general concept of release management doesn’t really change between ITIL (IT Infrastructure Library) and DevOps, there are a few ways that the process differs.

Over the past few years, cloud computing has enabled agile, dynamic management of software and hardware components, on-demand. Nowadays, we can define our desired infrastructure in as little as a few lines of code, and we can provision real servers on cloud providers like AWS or Azure. Terraform is an open-source infrastructure-as-code (IaC) tool that has become the de facto solution for provisioning one aspect of those components.

OpenStack vs AWS is a discussion that almost every organisation must conduct when adopting a cloud strategy. This is because OpenStack and AWS are undoubtedly some of the most popular cloud technologies in both public and private cloud space. While AWS is the most popular commercial cloud platform, OpenStack remains its most popular open source equivalent. Both have their own pros and cons. But which one is better for you?