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4 min readAug 13, 2025
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Logging is the heartbeat of any application. Without logs, you’re flying blind — no visibility into what’s going wrong, what’s performing well, or what’s breaking under load. Automating the collection of those logs into Datadog gives you a single place to search, visualize, and alert on everything your systems are doing.
In this guide, we’ll build a tiny Python app and set up the Datadog Agent to forward its logs automatically to Datadog. The goal is not to build something complex, but to show the exact steps that connect an application’s logs to Datadog in a reliable, repeatable way.
By the end, you’ll have:
- A working local app producing logs.
- Datadog showing those logs…
Member-only story
4 min readAug 13, 2025
–
Press enter or click to view image in full size
Logging is the heartbeat of any application. Without logs, you’re flying blind — no visibility into what’s going wrong, what’s performing well, or what’s breaking under load. Automating the collection of those logs into Datadog gives you a single place to search, visualize, and alert on everything your systems are doing.
In this guide, we’ll build a tiny Python app and set up the Datadog Agent to forward its logs automatically to Datadog. The goal is not to build something complex, but to show the exact steps that connect an application’s logs to Datadog in a reliable, repeatable way.
By the end, you’ll have:
- A working local app producing logs.
- Datadog showing those logs in real-time.
- An understanding of why this setup is valuable and how to adapt it to your own systems.
Why Automate Logs to Datadog?
Before jumping into the “how,” it’s worth understanding why you’d want to automate this at all.
- Centralized visibility — Instead of chasing logs across multiple servers, containers, or local files, you can query them in one place.
- Unified analysis — Datadog automatically…