It's been a decade since Marc Andreessen declared that software is eating the world, and it is still hungry. Customers expect software solutions for every need, driving digital transformation in every analogue industry. Software quality is now fundamental to company reputation, directly affecting customer satisfaction, brand and overall business success.

Earlier, applications were mostly monolithic, meaning that several programs were written in the same language and placed in the same web stack. However, it is no longer the case today. Today, every software is comprised of several small application programs coming together each providing a service of its own. These applications are what we call microservices.

Containerized microservices have been the gold standard for cloud computing since they replaced the monolith architecture over a decade ago. The flexibility, scalability, and velocity they enable for teams make them an obvious choice. Yet, a strict interpretation of one service for one function doesn’t quite serve everyone, especially when architectures get large. We’ll discuss how flexibility in service architecture might be the way to go.

Proponents of microservices claim more development velocity and reliability; more comprehensive test and vertical or horizontal scale with a container orchestrator; tons of flexibility around tool choice. They’re not wrong: When you build with a microservices architecture, you’re likely going to see cost improvements early in your software development life cycle (SDLC), driven mostly by the decoupling of services.

Software complexity makes it harder for teams to rapidly identify and resolve issues. IT service management has evolved from an afterthought to a central part of DevOps. Microservices architectures are prone to delay or missed identification of such concerns. Monitoring mechanisms need to keep up with these complex infrastructures. Maintaining reliability and performance while harnessing this complexity requires a considered, data-driven approach.

From its initial appearance in the dev-tools space, GenAI has had an outsized impact on how developers approach day-to-day tasks (just ask any developer about when they first started using GitHub’s copilot). While any risks are still being evaluated—like potential for introducing anti-patterns or inadvertently running afoul of compliance requirements, many engineering teams have successfully implemented GenAI with measurable gains in collaboration and productivity.

The concept of observability has become a cornerstone for ensuring system reliability and efficiency in modern software engineering and operations. Observability, beyond its traditional scope of logging, monitoring, and tracing, can be intricately defined through the lens of incident response efficiency—specifically by examining the time it takes for teams to grasp the full context and background of a technical incident.

Companies obsess over end user experience, whether it is Amazon’s customer-centric innovation or Steve Jobs suggesting starting with the customer experience and working backwards to technology. But as our world becomes more knowledge-based and digital, we also need to consider the most important stakeholder on the payroll - software engineers.

Observability tools help engineering teams understand the health and behavior of software. But the term “health” in the context of this type of tooling is fairly narrow in scope—pertaining to real-time performance, reliability, and availability. While these are three important metrics to monitor, they’re lagging indicators of bigger issues happening upstream.

You might have heard of Spotify Backstage (backstage.io), an open-source option for building internal developer portals. But what is Spotify Backstage? This article will help answer your questions about Backstage and explain important considerations to think about if you’re deciding whether to adopt in your team.