This article details how Yiguo Group migrated from a single‑server SQL Server setup to a distributed TiDB + TiSpark architecture, highlighting performance gains, HTAP capabilities with TiFlash, ETL differences, and future data‑platform considerations.
This article surveys the recent surge in real‑time data warehouse construction, explains why low‑latency analytics are needed, and compares the architectures and technology stacks of Alibaba, Zhihu, Meituan, and NetEase, highlighting the role of open‑source OLAP engines such as Druid and ADB.
This article details Zhihu's real‑time data warehouse evolution, describing the 1.0 Spark Streaming architecture, its limitations, and the 2.0 redesign that introduces Flink, layered data models, streaming and batch ETL, metric storage choices, and future roadmap for scalable, low‑latency analytics.
This article describes the motivation, technology selection, implementation details, and practical challenges of building a real‑time data warehouse using Flink, covering stream ingestion, data cleaning, dimension‑table joins, state backend choices, and operational lessons for large‑scale streaming pipelines.
This article describes the motivation, technology selection, implementation details, and encountered challenges of building a real‑time data warehouse using Flink, covering streaming computation, code examples, dimension‑table caching, state backend choices, and best practices for production deployment.
This article details Zhihu's evolution of its real-time data warehouse, covering the 1.0 version built on Spark Streaming, the 2.0 upgrade using Flink Streaming SQL, architectural layers, ETL processes, and future directions such as streaming SQL platformization and automated result validation.
This article provides an in‑depth technical overview of Alibaba's AnalyticDB, covering the challenges of massive real‑time analytics, the cloud‑native multi‑tenant architecture, data model, import/export capabilities, high‑performance SQL parser, the Xuanwu storage engine, Xihe compute engine, optimizer, GPU acceleration, and elastic scaling features.
This talk presents Yanxuan’s real‑time data warehouse built on Flink, covering background challenges, overall architecture and implementation, data quality measures, monitoring, and practical application scenarios, while highlighting design goals of flexibility, high development efficiency, and stringent data quality requirements.
This article explains how Meituan tackled growing real‑time data demands by redesigning its streaming platform, adopting a layered real‑time data warehouse architecture, selecting storage and compute technologies such as Cellar, Elasticsearch, Druid and Flink, and sharing practical tips on dimension expansion, joins, and aggregation to achieve higher throughput and lower latency.
Meituan replaced its Storm‑based pipeline with a four‑layer real‑time data warehouse powered by Flink, using hybrid storage (Cellar KV, Elasticsearch, Druid, MySQL) to deliver low‑latency, high‑throughput services, dramatically simplifying SQL‑driven development, unifying metrics, cutting compute costs, and paving the way for offline‑grade accuracy and reliability.
The article details the evolution from an offline to a real‑time HBase data warehouse, covering business scenarios, the use of Maxwell for MySQL‑to‑Kafka ingestion, Phoenix for SQL access, CDH cluster tuning, monitoring, and several production case studies.
