In today’s dynamic data environments, tables and schemas are constantly evolving and keeping semantic layers up to date has become a critical operational challenge. Manual updates don’t scale, and delays can quickly lead to broken dashboards, failed pipelines, and lost trust.
We’ll show how to harness Apache Airflow 3 and its new event-driven scheduling capabilities to automate the entire lifecycle: detecting table and schema changes in real time, parsing and interpreting those changes, and shifting left the updating of semantic models across dbt, Looker, or custom metadata layers. AI agents will add intelligence and automation that rationalize schema diffs, assess impact of changes, and propose targeted updates to semantic layers reducing manual work and minimizing the risk of errors.
We’ll dive into strategies for efficient change detection, safe incremental updates, and orchestrating workflows where humans collaborate with AI agents to validate and deploy changes.
By the end of the session, you’ll understand how to build resilient, self-healing semantic layers that minimize downtime, reduce manual intervention, and scale effortlessly across fast-changing data environments.
Scott Mitchell
Data Governance Practice Lead at Qbiz Inc.