Laís Carvalho
Laís Carvalho is a Developer Advocate & Growth Marketing at Pydantic, where she works on Logfire and the Pydantic ecosystem. She previously championed Kedro at QuantumBlack (McKinsey) and Quansight. Her throughline: making complex developer tools feel approachable. She's currently pursuing a postgraduate in Innovation and Enterprise Development at Trinity College Dublin.
An EuroPython Society Fellow and 2025 PSF Community Service Award recipient, Laís has served on the boards of EuroPython and Python Ireland, and co-founded HumbleData — a nonprofit mentoring underrepresented minorities in Python and Data Science.
She advocates for monitoring tools that help developers sleep better at night and believes diverse teams build better systems. Outside of tech, you'll find her experimenting with watercolour painting or on an improv stage.
Session
Most Django developers have been there: something is slow, users are complaining, and the first instinct is to sprinkle print() statements or scroll through server logs hoping for a clue. Without proper observability, diagnosing performance bottlenecks in a Django application (or any application, for that matter) is guesswork.
Observability is the practice of understanding what is happening inside your application by looking at the signals it produces: traces, metrics, and logs. While the concept is well-established in infrastructure and DevOps circles, it remains under-explored in the day-to-day workflow of many Django developers. Yet Django's middleware architecture, ORM, and request/response cycle make it particularly well-suited for instrumentation.
In this talk, I will walk through how to add observability to a Django app using Pydantic Logfire. I'll cover the Four Golden Signals of observability (latency, traffic, errors, and saturation), explain why they matter for your Django app, and show how to expose them with minimal setup. Through a live demo, you will see how to use metrics dashboards in action to monitor your system. You'll also see how to leverage AI to query your logs and traces in natural language or with SQL.
Attendees will leave this talk with a practical, reproducible workflow for adding observability to their own Django projects, along with an understanding of which signals to monitor and why.
Prerequisites: Attendees should be comfortable with Django basics. No prior experience with observability tooling is required.
Outline breakdown:
* 0–3 min The problem: Why print() and log scrolling don’t scale.
* 3–7 min Observability 101 for Django developers: The Four Golden Signals (latency, traffic, errors, saturation) explained with Django-specific examples.
* 7–12 min Setting up Observability - Instrumenting observability in a Django project: logging handler, Django instrumentation, PostgreSQL instrumentation. Live demo: showing traces appearing in the Live view.
* 12–19 min Built-in dashboards and system metrics: Enabling system metrics. Walkthrough of System Metrics dashboard. Mapping charts to the Golden Signals (CPU → saturation, process count → traffic). Building custom error charts with SQL queries. Live demo.
* 19–23 min Putting it all together: How to think about what to monitor. Practical tips for setting thresholds and correlating signals across dashboards.
* 23–25 min Wrap-up: Summary, resources, and where to go next.
* 25-30 min: Q&A