Operations | Monitoring | ITSM | DevOps | Cloud

In this series, we’ve introduced key HashiCorp Vault metrics and logs to watch, and looked at some ways to retrieve that information with built-in monitoring tools. Vault is made up of many moving parts, including the core, secrets engine, and audit devices. To get a full picture of Vault health and performance, it’s important to track all these components, along with the resources they consume from their underlying infrastructure.

In Part 1, we looked at the key metrics for monitoring the health and performance of your HashiCorp Vault deployment. We also discussed how Vault server and audit logs can give you additional context for troubleshooting issues ranging from losses in availability to policy misconfiguration. Now, we’ll show you how to access this data with tools that ship with Vault.

A few months ago, I was asked if I wanted to develop an open-source Terraform provider. Eleanor Saitta, principal at Systems Structure Ltd, had a client who was setting up JFrog Xray across their Github repositories but didn’t want to configure each repository by hand. As an SRE who enjoys working on projects that automate away those sorts of pain points (and someone who works extensively with Terraform during their day job), this sounded like an interesting project to work on.

Serverless computing is one of the fastest-growing areas in software development, along with hybrid clouds and using multi-cloud strategies. The global serverless architecture market is expected to grow at a CAGR of 16.2% in 2021 through 2026. It will reach $10.29 billion from $4.2 billion in 2020, according to MarketWatch. The serverless approach promises to help developers beat the cons of self-managed and even virtual server infrastructure. But is serverless right for you?

Last week DevOps Institute’s Chief Ambassador, Helen Beal, and Moogsoft’s own Chief Evangelist, Richard Whitehead, continued to follow the exploits of DevOps Engineer Sarah and her journey towards AIOps and Observability enlightenment.

In this video with with Kohsuke Kawaguchi (KK) from Launchable and Viktor Farcic we talk about testing K8s applications. Do we really need to write all the tests every time we make a change to the source code or make a release? That could take minutes or even hours. Wouldn’t it be better to run only the tests related to the changes we are making or the phase of the lifecycle of an application? Is the future of testing in AI and ML?