Most observability workflows break down somewhere between collection and understanding. Raw logs often require manual effort to parse, structure, and interpret, slowing teams down when answering even basic questions.

In this session, we will explore how Datadog is evolving log management to make logs useful, automatically. We'll take a behind-the-scenes look at how raw logs are transformed into clean, structured, and meaningful data, using automatic parsing and attribute extraction to turn logs into actionable signals.

We'll also explore how this enables faster, more intuitive ways to explore, search, and understand logs without relying on deep schema knowledge or complex queries. By focusing on automated value extraction, teams can spend less time preparing data and more time understanding their systems.

This session will show you how logs are evolving from data that you manage into insights that you use.