Apache Paimon combines a lakehouse table format with an LSM-tree update engine to handle continuous updates, historical snapshots, and multi-engine access in real-time data warehouses, reducing the need for separate Kafka, Hive, and OLAP systems.
The article analyzes how StarRocks, Fluss, and Paimon can replace the traditional Lambda architecture with a unified lakehouse solution that delivers second‑level data freshness, consolidates storage and compute, and provides native read/write paths for high‑performance real‑time analytics and large‑scale ETL.
The article details how Taotian Group’s senior data engineer Zhu Ao designed a lake‑stream integrated architecture using Fluss for second‑level real‑time storage, Paimon for minute‑level lakehouse persistence, and StarRocks as a unified OLAP query layer, achieving over 50% faster development and more than 80% cost reduction.
This article presents a lake‑stream integrated solution that combines Apache Fluss, Apache Paimon, and StarRocks to achieve second‑level data freshness, tenfold storage cost reduction, and a single‑query access pattern for both real‑time and historical data, detailing its architecture, advantages, query modes, and future roadmap.
The Streaming Lakehouse Meetup (May 27) explores breaking data silos by unifying structured tables, images, video, audio, and high‑dimensional vectors through StarRocks‑Paimon‑Fluss integration, covering multimodal fusion retrieval, vector search internals, native reader/writer performance gains, and real‑world ANN indexing practices.
The article presents a Flink‑Fluss‑LLM architecture that captures full‑link agent events via a non‑intrusive hook, combines semantic AI inference with deterministic CEP rules, and delivers millisecond‑level alerts for malicious user detection, tool result poisoning, and chain‑attack risk mitigation.
In the era of rapid AI growth, Fluss offers a unified lake‑stream architecture that tackles data quality, timeliness, scale, and multimodal challenges by tightly integrating Flink streaming with a high‑performance data lake, enabling seamless real‑time and batch analytics for AI workloads.
The Fluss project, an open‑source next‑generation stream storage engine donated by Alibaba, has entered the Apache Software Foundation incubator, offering columnar streaming, real‑time updates, lake‑flow integration, impressive performance metrics, and a growing global developer community.
This article summarizes a talk by Alibaba Cloud senior engineer and Flink Committer Luo Yuxia on the challenges of separating lake and stream storage, introduces the Fluss lake‑stream unified architecture, explains its technical benefits such as second‑level data freshness, unified metadata, efficient changelog generation, and outlines future plans for broader ecosystem integration.
Fluss, an open‑source real‑time stream storage project from Alibaba, integrates columnar formats and low‑latency updates with Apache Flink to address the limitations of traditional Kafka‑Flink pipelines, offering high throughput, low cost, and seamless lakehouse support for modern data analytics.
