This article presents a comprehensive overview of Iceberg MOR principles, Arctic‑based performance optimizations, benchmark evaluations using CH‑benchmark, and future roadmap items, highlighting how various file‑type strategies, self‑optimizing mechanisms, and task balancing improve real‑time data lake query efficiency.
This article presents a comprehensive overview of Arctic's streaming capabilities on Flink, detailing its mixed‑format architecture, core principles, benchmark comparisons with Iceberg, future development plans, and a Q&A session covering implementation nuances and performance considerations.
This article introduces the Arctic lakehouse management system built on Apache Iceberg, explains Iceberg’s core principles, format versions, and real‑world implementations at NetEase, and details Arctic’s automated table optimization, governance workflows, and future development plans.
This article explains the challenges of a Lambda‑architecture data pipeline, introduces NetEase’s Arctic lakehouse built on Apache Iceberg, details its table‑store design, optimization cycles, consistency mechanisms, real‑time features, practical use cases, and future roadmap, highlighting its advantages over similar solutions.
This article introduces NetEase’s Arctic, a real‑time lakehouse system built on Apache Iceberg that unifies streaming and batch processing, explains the challenges of Lambda architecture, details Arctic’s features such as change/base stores, hidden queue, transaction handling, and shares internal practice cases and future roadmap.
Arctic, NetEase’s streaming lakehouse built on Apache Iceberg, unifies streaming and batch workloads with millisecond‑level latency, Hive compatibility, and built‑in message‑queue support, delivering CDC, upserts and OLAP without a Lambda architecture, as demonstrated by real‑time processing of 2 PB of Hive data for Cloud Music.
This article introduces NetEase's internally incubated data lake project Arctic, explains the concept of data lakes, outlines NetEase's specific requirements for a unified streaming‑batch platform, details Arctic's core architecture, storage strategy, data‑merge mechanisms, current achievements, and future development plans.
This article explains the evolution from traditional data warehouses to modern lakehouse architectures, introduces the Arctic system’s dynamic hash tree for fast update/delete, describes file splitting with sequence/offset ordering, and compares copy‑on‑write versus merge‑on‑read techniques for achieving low‑latency analytics.
