As digital transformation and the consequent move towards cloud-native continues to accelerate, and customer demands increase, traditional approaches to debugging and troubleshooting are limited and insufficient. Developers must quickly understand the relationships between user sessions, topology, and end-to-end transactions during incidents.

Logs are the core of the human-machine interface for software developers and operators. Historically, they are very much like caveman paintings. They were our first attempt to express and understand how our software was working. For several decades, logs were an island of calm in a rapidly changing technological ecosystem. Logs remained the same even as software services became web-based and grew in scale.

If you were to wake me up at 3 am and ask me how Rookout differs from logs (or other pillars of Observability, for that matter), my answer would have focused on the agile nature of live debugging. I would have explained that Non-Breaking Breakpoints empower you to decide in real-time what data you need. Traditionally, you would use whatever logs (or metrics and spans) happen to be in the code and work backward from there.

Every engineering leader wants to build a high-performance team that can set the standard for velocity, quality, and innovation. Yet, to do that, you need to focus on creating a culture of continuous improvement where everyone in the team is committed to learning and growing.

R&D managers, when first stepping into the role, tend to make the mistake of choosing one of two extreme management approaches. Each of these comes with its own set of challenges – and its own set of organizational waste. And that’s the last thing you want to create as a manager.

This blog is a follow-up to our earlier discussion of multibranch pipelines and how they can help streamline software development processes. There we explored the benefits of managing pipelines in the same repository as code and how that gives developers the ability to version their pipelines alongside their code, ensuring they remain in sync.

Making mistakes is a part of life. It’s how we learn and grow. However, many people – and teams – struggle with the aftermath of making a mistake, and it can be challenging to recover quickly.

As engineering leaders, we all struggle to find the balance between creating cool technology and useable technology. Let’s be real. We all want to create cool things. It’s in our nature. But what’s the point of doing so when no one will use it? It’s crucial to remember that the ultimate goal of our work is to create something helpful and effective. Our work has to have a purpose.

Lately, everyone has been jumping on the cloud transformation bandwagon, which isn’t surprising, because when it comes to tech, you don’t want to find yourself behind, stuck with your dusty old monoliths. Just kidding – we love ourselves some good old monolith architectures – but there’s no comparing to dynamic, cloud-native technology.

When it comes to understanding what’s happening in your code – and your service health, specifically – business and code-level metrics are key. While most developers are experts in their own code, that doesn’t mean that they’re also experts on the metrics, statistics, or distributed tracing from the code they’re working with. The code-level metrics they need are tough to set up and it’s something that no one else can do for them.