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Health check endpoints generate thousands of identical, useless spans per day. Here are two production-ready approaches to filter them from your Python OTel traces — and the correctness trap most implementations miss. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

Last updated: March 2026 Python applications built on Django, Flask, FastAPI, and other frameworks have the same monitoring needs as applications built in any other language: you want to know which endpoints are slow, why the database is getting hammered, what errors are firing in production, and ideally all of that in a form that does not require three separate tools to reconstruct a single incident.

InfluxData prides itself on prioritizing developer happiness. A key ingredient to that formula is providing client libraries that let users interact with the database in their chosen language and library. Data analysis is the task most broadly associated with Python use cases, accounting for 58% of Python tasks, so it makes sense that Pandas is the second most popular library for Python users.

Serverless platforms have revolutionized the way developers build and deploy APIs, eliminating the need to manage servers or underlying infrastructure. With serverless, you can focus entirely on your application logic and let the platform handle scaling, availability, and maintenance. Scaleway Serverless Functions is a flexible serverless platform that makes it easy to deploy lightweight APIs and background jobs in the cloud.

Three frameworks dominate Python backend development in 2026: Django, FastAPI, and Flask. This guide helps you choose between them (plus specialized alternatives like Falcon, Tornado, and Litestar) using a simple decision tree. Answer three questions about your project, understand each framework's strengths, and pick the right tool for your needs.

Continuous profiling has established itself as core observability practice, so much so that we’ve referred to it as the fourth pillar of observability. But despite the capabilities and growing adoption of continuous profiling, it can still be confusing to approach profiling as a newcomer and correctly apply it to different troubleshooting scenarios.

Python is deeply embedded in modern infrastructure, but many organizations continue to run outdated Python across critical systems. Sticking with older runtimes may seem harmless, but it quickly piles up technical debt as teams spend more time maintaining fragile code and applying workarounds. Over time, that debt translates into a high financial drain.

‍ Like many developers, I’ve spent the last year adjusting to a world where I’m spending more keystrokes arguing, or, “explaining” to an agent the code I’d like to be written. No longer must we toil, writing every line code by hand like our great grandparents probably did.