Building, testing and deploying software is a time-consuming process that many organizations aim to minimize by automating repeatable work wherever possible. To do so, many organizations are utilizing a continuous integration, continuous delivery (CI/CD) philosophy in combination with cloud native tools like Kubernetes to develop and deploy software at scale.

Any organization that’s keeping up with today’s sharp rise in business demands (or better yet, getting ahead of the game) is doing so by getting innovative and jumping at the chance to do things differently. They’re not relying on the old ways or trying to use their existing toolbox. Instead, organizations are looking to the newest technologies and means of adding efficiency to as many day-to-day functions as possible.

Let us consider a scenario where a Logic App is used to communicate with SharePoint through API connections, known as connectors. When configuring the connector, it communicates with Azure AD, retrieving a username and password and continuously refreshing the authentication token. When the Logic App calls the connector, it performs operations like uploading files to SharePoint.

Ensuring software availability is essential for any SaaS company—including Gremlin. To do that, our teams need to identify the reliability risks hiding in our systems. That’s why our development, platform, and SRE teams use Gremlin regularly to perform Chaos Engineering experiments, run reliability tests, and track the reliability of our systems against our standards. Along the way they’ve picked up a thing or two about how to find and fix reliability risks with Gremlin.

Writing clean and efficient code can be time consuming, but with the right tools, it can be much easier. In this blog post, we will explore how to use Copilot for code autocompletion and JFrog Artifactory for your package management.

Managing containerized applications efficiently in the dynamic realm of Kubernetes is essential for smooth deployments and optimal performance. Kubernetes empowers us with powerful orchestration capabilities, enabling seamless scaling and deployment of applications. However, in real-world scenarios, there are situations that necessitate the restarting of Pods, whether to apply configuration changes, recover from failures, or address misbehaving applications.

This article will outline what Redis database monitoring is and how to set up a Redis database monitoring system with MetricFire. Then we’ll show what the final graphs and dashboards look like when displayed on Grafana. We will be using Prometheus and Grafana to power the monitoring, and we'll use a simulated Redis DB to generate the data for the Grafana dashboards. ‍ ‍