Coralogix: From Data Chaos to Governed AI-Native Observability with Dataspaces and Datasets

The challenge isn’t finding a better observability AI model. It’s creating the right structure underneath it. Teams often struggle with performance bottlenecks, permission complexity, unclear cost attribution, and increasingly chaotic schemas as their environments scale.

Join this session to learn how Coralogix’s new segmentation layer helps bring order to observability data. Discover how our AI-native architecture creates a structured, contextual data lake that enables faster insights, stronger governance, and better outcomes for both teams and AI agents.

By the end of the session, you will learn how to:

How Coralogix segments a single data stream into governed, scoped datasets without changing how you send data
Streaming datasets: expression-driven routing by any field, any business logic
Summary datasets: turning expensive queries into persistent, reusable assets
System datasets: observability on your observability – query performance, schema health, audit trails as queryable data
Live demo: creating datasets, routing data, and querying results
Real-world use cases from customers and TAMs