Observability has become increasingly important for IT professionals as the complexity of modern systems has grown. In the past, IT environments were typically composed of a few servers and applications that were all running on-site. However, with the rise of cloud computing, IT has become more distributed, with applications and services running on a wide variety of infrastructure and platforms.

Data routing is a crucial but complex task for companies of all sizes. Ensuring that the right data is sent to the right tools can be a time-consuming and difficult process, and when things go wrong, it can have costly consequences. This is why having a robust data routing strategy is essential for any organization.

Whether you’ve been following along with our Authors’ Cut series or doing some self-paced learning, our O’Reilly book Observability Engineering is one of the best resources for jumpstarting your observability journey. It serves as a blueprint to help you understand and map out the technical and cultural requirements of implementing observability into your organization.

By incorporating observability into your stack, you can better understand how your complex infrastructure operates, reduce downtime, and empower developers to quickly identify and fix problems. However, it now takes considerably more work, time, and money to build up observability for your infrastructure and applications. Over half of the firms polled employ eight or more observability solutions, according to a 2022 Splunk survey.

Not to put too fine a point on it, but we think distributed tracing gets a very bad rap for being too complicated and labor-intensive. We’re here to show you three ways you can jumpstart a distributed tracing effort, starting small and expanding as it makes sense. These examples involve only a little code and perhaps a bit of a mindset change. Starting small with distributed tracing can even be fun, because who doesn’t like getting customized results without much work?