Data teams are under growing pressure to deliver trusted, on-time data to support analytics, AI, and business decision-making. But when pipelines fail silently, data arrives late, or schema changes go unnoticed, the downstream impact is costly and often hard to trace. Traditional observability tools were not designed for this. They focus on the warehouse and miss the orchestration layer where issues actually begin.

In this session, we explore how the next generation of observability is shifting into the orchestration layer to help teams detect and resolve issues faster, reduce blind spots, and increase trust in their data.

We outline what is driving this shift, what capabilities to look for in modern observability solutions, and how orchestration-native approaches help improve reliability and reduce operational overhead.

We also share how platforms like Astro, the orchestration-first DataOps platform built on Apache Airflow, and Astro Observe, its built-in observability layer, support this modern approach and help teams build, run, and observe trusted pipelines at scale.

What You’ll Learn:

• Why traditional observability tools often miss upstream pipeline issues that affect data trust
• How orchestration context improves visibility, reliability, and resolution speed across the stack
• What to consider when evaluating next-generation observability solutions that reduce complexity and cost
• How Astro and Astro Observe provide a unified, Airflow-native platform for pipeline health, quality, and performance monitoring