The film “The Martian” starring Matt Damon depicts an astronaut stranded alone on planet Mars who uses his knowledge of botany, engineering, & science to survive the red planet's harsh conditions. Damon's character in the movie showed how to think outside the box with creative solutions for growing food, recycling water, calculating orbital trajectories to return to Earth, and modifying his spacecraft to launch-off the surface of Mars & gets intercepted by a rescue team. The NASA team that was tasked to bring Matt back, used data-driven analytical approach to overcome each obstacle they faced. This mirrors the vital support an observability engineer provides to a DevOps team throughout the software development & deployment life cycle. During this movie, when Matt said,

“I'm going to have to science the sh*t out of this!!"

is an intriguing message in the context of observability engineering as this embodies the spirit of innovation & relentless determination to solve problems.

Observability engineering involves carefully monitoring and logging key metrics at each stage of development, testing, and production. Just as Damon's character leverages data insights to triage the problems, observability gives technology teams the meaningful insights they need to debug issues, optimize performance, and understand how new features are being used. This transparency helps developers’ course-correct when things go wrong and validate that release build will be stable and reliable. By shedding light on what is happening behind the scenes, observability engineers empower businesses to confidently accelerate innovation while avoiding unintended incidents that cause costly downtime

A good litmus test to determine if you need observability into your architecture & modernization efforts is to simply ask some basic questions.

• Can you analyze the architecture & inner workings of your application to provide insights about any anomalies or outliers detected?
• Is the ITOps team spending excessive time to incident analysis? The details relayed to the development team lack crucial information necessary for incident resolution.
• Can you explain what could be causing an unusual pattern in outliers in your microservice architecture deployed in the cloud?
• For a critical business transactions that is showing degradation, can you see what specific data or user attributes caused those outliers?
• Does compilation & integration errors in the development cycle causing high rate of build failures, which in-turn causing significant time into rectifying the issues?

Unlike legacy ways of monitoring a system, where static alerting provides no context of why an incident happened, neither the user & scope of the impact, observability engineering helps you seek answers to these questions. It requires evolving the way you think about gathering the data needed to debug, but also able to query the data effectively. It goes beyond eyeballing shapes on a prebuilt ITOPs dashboards or rolling out an AIOps data-lake solution to boil the data ocean. The first step of building observability is to bake-in the application meta data into your telemetry & your structured logging & then follow up with stitching the logs with the trace components so that the entire debugging process become seamless. Once you start harvesting this enriched data, the ability to apply analytics, ask questions and seek answers from traces, metrics, & log events to illuminate the unknowns in applications & systems, as this becomes a steppingstone towards maturing & advancing your SRE capability model. This is a practice that should be done as part of the shift left movement. It begins with working with the development team to instrument application code logic, infrastructure & business context meta-data into the trace, events, logs & metrics. This will help developers not only debug faster, but also enables testers to identify failures in their testing cycles, eventually helping business to prioritize defects based on measured impact

With rich telemetry data available across environments, DevOps team achieve major lead time reductions by shifting observability practices into CI/CD workflows. This enables developers to receive feedback, on code quality and test effectiveness even before reaching the staging or production stages.

By adopting this strategy, you will experience a multitude of advantages. From Incident-Response-Time reduction to lead time for change, improving system downtime and Availability% SLO, to MTTR, & developers’ productivity, from hours to minutes. When you consider all these improvements collectively, you can see a consistent month-over-month enhancement in your KPI reporting across the board.

So in summary, observability-driven development is quickly becoming the go-to strategy in today's tech world. Whether you are embarking on a modernization journey or tasked to cut tech debt, observability engineering will give your teams the power to spot problems right where they start. By embedding observability tooling & practices in your code & infrastructure, and closely examining the results, teams can zero in on what matters most. This way, tech leaders stay on top of their game, tackling issues bit by bit and avoiding the kind of major setbacks or risks that can sneak up and bog down progress in a project.