From Batch Refresh to Continuous Intelligence: Incremental Computing in Apache Doris 5.0

About this session

AI agents need more than fresh source data—they need business metrics and analytical results that keep pace with change. Yet maintaining those results through repeated batch refreshes introduces delays and costly recomputation. This keynote explores a central question: How do we keep intelligence continuously fresh? Starting with real-world business scenarios, we introduce the incremental computing architecture in Apache Doris 5.0: Row Binlog and Table Stream as the foundation for capturing and consuming changes, with Flink-based pipelines for downstream processing and incremental view maintenance (IVM) for automatically maintaining analytical results within Doris. Practical demos show how inserts, updates, and deletes translate into correctly updated business results, while end-to-end latency measurements reveal how quickly changes reach downstream systems and derived metrics. We conclude with a user case study, connecting the architecture to measurable improvements in data freshness and computational efficiency—and showing what it takes to keep the data behind intelligent applications continuously up to date.

Speaker

Shilin Wu

Solutions Architect · VeloDB

LinkedIn ↗ · Shilin Wu