This article deep-dives into Apache Fluss architecture, detailing its Master-Worker design, dual LogStore and KvStore engines, tablet-based sharding, tiered hot/cold storage on remote object stores, and Flink-centric client integration, explaining how these components unify streaming and lakehouse workloads.
This article explains the concept of a real‑time lakehouse, compares it with traditional batch warehouses, introduces Apache Paimon and its innovations such as native upserts, LSM storage, tags and branches, and showcases multiple enterprise use cases that demonstrate its low‑cost, low‑latency stream‑batch integration.
This article presents Xiaomi's real-time lakehouse architecture, outlines its current challenges, introduces Apache Paimon and several use‑case scenarios—including stream join optimization, streaming upserts, and lookup joins—while discussing expected benefits and future directions for a more efficient, unified data platform.
This article presents a comprehensive overview of Apache Paimon, covering the concept of real‑time lakehouses, the underlying technologies such as LSM and merge‑on‑write, practical application cases across enterprises, and the latest frontier features like tags, branches, and advanced indexing, illustrating how Paimon bridges batch and streaming workloads in modern big‑data ecosystems.
