1.	Background & Motivation
•	Flink SQL challenges at scale: large state, long joins, complex maintenance
•	Business use case: automotive data warehouse and alerting systems
2.	State Externalization with Delta Join and Merge Engine
•	Delta Join: external dimension tables with up-to-date snapshots
•	Merge Engine: external upsert tables for aggregation results
•	How we minimized internal state while ensuring correctness and latency
3.	The Role of Dynamic Tables
•	Intermediate table explosion: a hidden cost of state externalization
•	How Dynamic Tables automate schema creation, lifecycle, and data lineage
•	Empowering developers: write business logic, forget the plumbing
4.	Enabling Batch-Stream Unification
•	Lightweight Flink state: only track offsets
•	Same SQL logic for both real-time and backfill workflows
•	Practical benefits in backfills, audits, and reprocessing
5.	Results & Lessons Learned
•	Developer experience and productivity gains
•	Improved job resilience and observability
•	Where we’re going next: tighter integration with metadata and scheduling

Speakers:

Feng Jin is currently a member of the Compute Platform team at Xiaomi, where he is responsible for maintaining the internal Flink framework and building the company’s real-time lakehouse architecture. He has extensive experience in large-scale stream processing, Flink SQL optimization, and state management. He is also an Apache Flink committer and actively contributes to the open-source community.