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Didi Tech

Aug 26, 2020 · Big Data

Real-time Data Warehouse Construction at Didi: Architecture, Practices, and Lessons

To support Didi’s fast‑growing car‑pool service, a real‑time data warehouse was built using a streamlined layered architecture—ODS, DWD, DIM, DWM, and APP—leveraging Flink‑based StreamSQL, Kafka, Druid and ClickHouse to deliver minute‑level analytics, dashboards, monitoring, and cross‑business interfaces while planning unified meta‑store integration.

FlinkReal-time Data WarehouseStream Processing

0 likes · 20 min read

Real-time Data Warehouse Construction at Didi: Architecture, Practices, and Lessons

Didi Tech

Apr 30, 2020 · Big Data

Didi’s Real‑Time Computing Practices with Apache Flink and StreamSQL

Didi has unified its real‑time computing on Apache Flink, creating an enhanced StreamSQL service with extended DDL, built‑in parsers and UDX, supporting thousands of nodes, millions of jobs, and trillions of daily records, while addressing state management, high availability, multi‑language UDFs, and pursuing real‑time ML and data‑warehouse integration.

Apache FlinkBig DataDidi

0 likes · 13 min read

Didi’s Real‑Time Computing Practices with Apache Flink and StreamSQL

DataFunTalk

Apr 22, 2020 · Big Data

Didi's Real-Time Computing Practices with Apache Flink: Architecture, StreamSQL, and Operational Insights

Senior Didi technology expert Liang Li-yin shares how Didi leverages Apache Flink for large‑scale real‑time computing, covering service architecture, StreamSQL advantages, multi‑cluster management, task control, monitoring, meta‑store integration, challenges, and future plans such as high availability, real‑time ML, and unified batch‑stream processing.

Apache FlinkData EngineeringReal-Time Computing

0 likes · 14 min read

Didi's Real-Time Computing Practices with Apache Flink: Architecture, StreamSQL, and Operational Insights

Didi Tech

Dec 18, 2018 · Big Data

Evolution and Architecture of Didi's Real-Time Computing Platform

From early self‑built Storm and Spark Streaming clusters to a unified YARN‑based Spark platform and finally a low‑latency Flink system with extended CEP and StreamSQL capabilities, Didi’s real‑time computing platform evolved through three stages, delivering multi‑tenant isolation, rich SQL processing, and dramatically reduced development costs.

CEPFlinkReal-Time Computing

0 likes · 9 min read

Evolution and Architecture of Didi's Real-Time Computing Platform