When we work at it, professionals are pretty good at analysis. We can break down a simple system, look at its parts and their relations, and master it. Given enough time and teammates, we can analyze a very complicated system and fix it when it breaks. But complex systems don’t yield to analysis. We have to add another skill: sense-making. Complex systems have parts that learn and change, with relations that vary with state and history. They respond to and influence their environment.

Digital transformation is accelerating across organizations of all sizes around the world, and a survey by Mckinsey reveals COVID-19 accelerated the speed of adoption of digital technologies by several years.

Businesses are increasingly adopting distributed microservices to build and deploy applications. Microservices directly streamline the production time from development to deployment; thus, businesses can scale faster. However, with the increasing complexity of distributed services comes visual opacity of your systems across the company. In other words, the more complex your system gets, the harder it becomes to visualize how it works and how individual resources are allocated.

Developers are increasingly drawn to Functions-as-a-Service (FaaS) offerings provided by major cloud providers such as AWS Lambda, Azure Functions, and GCP Cloud Functions. The Cloud Native Computing Foundation (CNCF) has estimated that more than four million developers utilized FaaS offerings in 2020. Datadog has reported that over half of its customers have integrated FaaS products in cloud environments, indicating the growth and maturity of this ecosystem.

So you're used to debugging systems using a distributed trace, but your system is about to introduce a message queue—and that will work the same… right? Unfortunately, in a lot of implementations, this isn't the case. In this post, we'll talk about trace propagation (manual and OpenTelemetry), W3C tracing, and also where a trace might start and finish.

Distributed tracing enables you to monitor and observe requests as they flow through your distributed systems to understand whether these requests are behaving properly. You can compare tiny differences between multiple traces coming through your microservices-based applications every day to pinpoint areas that are affecting performance. As a result, debugging and troubleshooting are simpler and faster.

As organizations are adopting more of the FinOps foundation practices and trying to optimize their cloud-computing costs, engineering plays an imperative role in that maturity. Traditional troubleshooting of applications nowadays relies heavily on static logs and legacy telemetry that developers added either when first writing their applications, or whenever they run a troubleshooting session where they lack telemetry and need to add more logs in an ad-hoc fashion.

Systems going down because of an unforeseen incident? Got problems with your app or website? Is your audience missing out on products and services because your load times are too slow? Then monitoring and observability (and telemetry) should be of interest to you! In this long article, we’re covering everything! I’ll start with the concepts and how they work.

Using the cloud can be challenging. Everyone likes to tell stories about a surprise cloud bill they received and laugh, but let’s be honest: it’s happened to all of us. When dealing with complex cloud environments and cloud-native architectures, gaining visibility into the state of the environment is critical. Today, developers are looking to monitor how things are working and performing as part of every step in the software development life cycle.