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Mattermost Playbooks help software engineering teams orchestrate their work across all tools and teams to plan projects and hit milestones by uniting your tech stack through a single point of collaboration. We want to see how our community is leveraging Playbooks in their own tech stack and share your creations with everyone so the whole community benefits. We’re doing this by launching a new effort to commission original blog articles that show Playbooks in action.

In pursuit of staying competitive in the market, many organizations choose speed over quality resulting in the accumulation of technical debt. This blog will dive deep into the concept of technical debt and explore ways to remediate it.

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.

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.

In a previous article, we talked about how you could use BAM to do advanced management of dead lettered messages on the Azure Service Bus using a scenario like below. This article will show how you can set up this kind of example. Let’s assume your Order Processing functions are already in place, and your starting point is that sometimes you will get dead-lettered messages, and we need to implement the Send message to the BAM logic app and configure Serverless360.

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.

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.

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.

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.