category
Software Engineering
- #101 7 min
SRE Doesn’t Scale
We encounter a lot of organizations talking about or attempting to implement SRE as part of our consulting at Real Kinetic. We’ve even discussed and debated ourselves, ad nauseam, how we can apply it at our own product company, Witful. There’s a brief, unassuming section in the SRE book tucked away towards the tail end of chapter 32, “The Evolving SRE Engagement Model.” Between the SLIs and SLOs, the error budgets, alerting, and strategies for handling change management, it’s probably one of the most overlooked parts of the book. It’s also, in my opinion, one of the most important.
- #100 12 min
Structuring a Cloud Infrastructure Organization
Real Kinetic often works with companies just beginning their cloud journey. Many come from a conventional on-prem IT organization, which typically looks like separate development and IT operations groups. One of the main challenges we help these clients with is how to structure their engineering organizations effectively as they make this transition. While we approach this problem holistically, it can generally be looked at as two components: product development and infrastructure. One might wonder if this is still the case with the shift to DevOps and cloud, but as we’ll see, these two groups still play important and distinct roles.
- #92 13 min
Digitally Transformed: Becoming a Technology Product Company
More and more established businesses are attempting to reinvent themselves as technology companies. At the heart of this is the digital transformation, a journey many organizations are undertaking in order to better compete and serve their customers. As a result, companies are pouring tons of cash into digital transformation strategies. For some, this means broader adoption of agile or DevOps practices. For others, it’s modernizing product offerings or moving to the cloud. Regardless of the changes, many are struggling to find success transforming themselves due to low throughput, quality issues, or failing to deliver the right thing at the right time. In a few cases, digital transformation has ended in outright disaster.
- #91 17 min
Microservice Observability, Part 2: Evolutionary Patterns for Solving Observability Problems
In part one of this series, I described the difference between monitoring and observability and why the latter starts to become more important when dealing with microservices. Next, we’ll discuss some strategies and patterns for implementing better observability. Specifically, we’ll look at the idea of an observability pipeline and how we can start to iteratively improve observability in our systems. To recap, observability can be described simply as the ability to ask questions of your systems without knowing those questions in advance. This requires capturing a variety of signals such as logs, metrics, and traces as well as tools for interpreting those signals like log analysis, SIEM, data warehouses, and time-series databases. A number of challenges surface as a result of this. Clint Sharp does a great job discussing the key problems, which I’ll summarize below along with some of my own observations.
- #90 9 min
Microservice Observability, Part 1: Disambiguating Observability and Monitoring
“Pets versus cattle” has become something of a standard vernacular for describing the shift in how we build systems. It alludes to the elastic and dynamic nature of these (typically, but not necessarily) container-based systems with on-demand scaling and more transparent fault-tolerance. I’ve talked before about this transition before and specifically how it relates to monitoring. In particular, with these more dynamic, microservice-based systems, the conversation starts to shift away from traditional monitoring toward observability. In this series, I’ll describe that distinction, explain why it matters, and share some concrete tactical items for implementing observability in a microservice environment.