How StarRocks, Paimon, and Fluss Enable Multimodal Fusion Search in a Lakehouse

When data expands from traditional structured tables to images, video, audio, and high‑dimensional vectors, conventional data architectures struggle with siloed storage, slow retrieval, and limited analytics. This meetup addresses how to achieve unified storage, real‑time cross‑modal retrieval, and intelligent analysis.

Event details (May 27, 19:00‑21:00) focus on five core breakthroughs:

Multimodal fusion retrieval – how StarRocks stores and queries structured and vector data together.

Vector capability deep dive – a StarRocks committer explains the core vector search algorithms and hybrid query implementation.

Lakehouse performance boost – analysis of the Native Reader/Writer mechanism that removes real‑time read/write bottlenecks and dramatically improves throughput.

Lake‑flow integration – the latest architecture combining StarRocks, Fluss, and Paimon for ultra‑low‑latency data ingestion and analysis.

Best‑practice deployment – a case study of ANN index usage in a lakehouse and AIData vector retrieval experience.

StarRocks, an open‑source Lakehouse engine under the Linux Foundation (Apache License v2.0), has grown a global community with over 11,000 GitHub stars, 500+ contributors, and thousands of active users.

The engine enables "One Data, All Analytics" by supporting BI reporting, ad‑hoc queries, and customer‑facing analytics on a single data copy, delivering business value across more than 600 enterprises valued over 7 billion CNY, including banks, retailers, travel platforms, gaming, and manufacturing firms such as China Minsheng Bank, Walmart, Tencent, and Meituan.

Attendees are invited to join the live session, interact with StarRocks core committers and Alibaba Cloud senior engineers, and explore the technical breakthroughs and practical paths for building an AI‑era unified analysis architecture.