How Meituan Built a Scalable Real‑Time Data Warehouse with Flink

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.

Data ArchitectureFlinkMeituan

0 likes · 15 min read

How Meituan Built a Scalable Real‑Time Data Warehouse with Flink

Meituan Technology Team

Oct 18, 2018 · Big Data

Building a Real-Time Data Warehouse with Flink at Meituan

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.

Data EngineeringFlinkMeituan

0 likes · 16 min read

Building a Real-Time Data Warehouse with Flink at Meituan

DataFunTalk

Oct 14, 2018 · Big Data

Exploring Real-Time Data Warehouse Practices Based on HBase

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.

HBaseKafkaPhoenix

0 likes · 14 min read

Exploring Real-Time Data Warehouse Practices Based on HBase