In the current macroeconomic climate, it’s more important than ever for executive teams and CEOs to make the most of their resources. Organizations are expected to continually deliver innovative products and services in a rapidly changing environment, often with reduced engineering budgets. 75% of tech leaders fear displacement from competitors beating them to market, so speed and efficiency are top of mind.

Engineering leaders are accountable to shipping quality products on time and within budget. But a CEO's historical understanding of engineering operations has been pretty opaque: ships on time, or doesn't. And although DORA metrics are a good place to start, they don’t offer the complete picture needed for CEOs. With new tooling to support greater engineering efficiency, CEOs are learning that it's possible to get a richer picture of engineering productivity, quality, and velocity.

In recent years, microservices have emerged as a popular architectural pattern. Although these self-contained services offer greater flexibility, scalability, and maintainability compared to monolithic applications, they can be difficult to manage without dedicated tools. Kubernetes, a scalable platform for orchestrating containerized applications, can help navigate your microservices.

Today's article discusses how ephemeral environments can help in microservices testing. We will start by understanding the microservices testing challenges and will move to the solution. You will learn how to effectively test microservices at scale using ephemeral environments, ensuring the reliability of your distributed systems.

Microservices are increasingly used in the development world as developers work to create larger, more complex applications that are better developed and managed as a combination of smaller services that work cohesively together for more extensive, application-wide functionality. Tools such as Service Fabric are rising to meet the need to think about and build apps using a piece-by-piece methodology that is, frankly, less mind-boggling than considering the whole of the application at once.

Most Cortex customers begin their journey by populating their service and resource Catalogs, creating their first few Scorecards, and setting up any time-sensitive Initiatives. These features help bring visibility and accountability to internally developed software, revealing consistency issues and enabling teams to act quickly to resolve.

Every so often, somebody in the tech industry proposes a new way to quantify software engineers’ productivity. Most recently, a leading consulting firm published a report that has reignited a recurring debate—is it even possible to effectively measure developer productivity, and if so, how? Whereas functions like sales and support have had rigorous metrics for years, software engineering has long been the exception.

Today, I’ll cover Shift Left Monitoring: A Pathway to Optimized Cloud Applications and how left-shifted troubleshooting of Spring Boot code issues using observability tooling can avoid production issues, unnecessary costs and improve product quality. Shift-left is an approach to software development and operations that emphasizes testing, monitoring, and automation earlier in the software development lifecycle.