Operations | Monitoring | ITSM | DevOps | Cloud

You can subscribe to Break Things on Purpose wherever you get your podcasts. In this episode of the Break Things on Purpose podcast, we speak with Sam Rossoff, Principal Software Engineer at Gremlin.

The year is over, and the word ‘Observability’ has been one of the buzzwords that kept everyone checking throughout the year for deserving reasons. The organizations do not want to leave any stone unturned to maintain performance and offer robust services from ‘monitoring’ practices to ‘observability’, ‘telemetry’, and visibility capacities. So let’s get into the meaning of each term and understand how they are vital for business growth.

In part 1 of this series, we looked at common design principles and patterns for assembling microservices in serverless environments. But when it comes to building serverless applications, designing your architecture is only part of the challenge. You also have to ensure that each of your individual functions and services are secure, reliable, and highly performant—without incurring enormous costs.

Serverless has become an increasingly popular paradigm among organizations looking to modernize their applications as it allows them to increase agility while reducing their operational overhead and costs. But the highly distributed nature of serverless architectures requires developers to rethink their approach to application design and development. AWS-based serverless applications hinge on AWS Lambda functions, which are stateless and ephemeral by design.

With rising awareness and increasing calls for action, the pressure is on to improve your organization’s energy sustainability. While frequently overlooked, moving your IT workloads to the cloud is one of the quickest ways to achieve a material reduction in your carbon footprint. Organizations typically look to the cloud to improve agility, scalability, and reduce costs.

Not all cloud cost management tools are equal. Whether you’re in the process of evaluating cloud cost management vendors or already have a tool in place, here are 19 questions you should ask to ensure you have all the capabilities needed to maximize performance and minimize cost of your hybrid cloud deployment across the following.

Service virtualization (SV) has evolved as a popular technique and technology over the last decade. Traditionally, SV has primarily been used by testers to simulate other application components that the application under test interacts with. Typically, virtual services have been created and maintained by center of excellence (COE) teams.

In my prior blog, Continuous Service Virtualization, Part 1: Introduction and Best Practices, we offered an introduction to continuous service virtualization (SV) and discussed some key best practices. In this, the second and final post in the series, we will discuss the continuous SV lifecycle and how it helps to optimize DevOps and the continuous integration/continuous delivery (CI/CD) pipeline.

There is a lot of the art of the possible between the GitOps Engine, Argo CD, and the Application-as-Code platform, Shipa. In a recent blog post, we outlined the power of a one-line developer experience. Though if you are unfamiliar with ArgoCD, here is a guide to get you started with Argo CD and leveraging Shipa for your first deployment.

Before you can answer this question, I like to step back and take a good look at the history of computing. Mainframes and legacy client-server applications were monolithic—all the processing took place on a single set of hardware. As hardware grew cheaper, and especially after widespread hardware virtualization came to fruition, these trends gave way to the widespread development of distributed systems. I began to see this trend during my time as an architect at a large cable and telecom provider.