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The article details how Kuayue Group's big‑data center replaced Presto and ClickHouse with DorisDB, achieving sub‑second query latency, simplifying architecture, and enabling both online and real‑time OLAP analytics across millions of daily requests.
The article chronicles Lenovo Liancheng Zhida’s three‑stage evolution of OLAP engines—from early SQL Server scripts, through a Hadoop‑based Presto solution, to the adoption of DorisDB—detailing architecture, tool comparisons, implementation practices, and the performance and operational benefits achieved.
The article describes how Beike Real Estate consolidated multiple OLAP engines into a single DorisDB‑based platform, detailing the business challenges, DorisDB’s technical advantages, extensive performance and concurrency benchmarks, and the resulting improvements in stability, query speed, and operational simplicity across various business scenarios.
This article details TAL's transition from offline to real-time data warehousing, describing business drivers, pain points, architectural evolution through Hive, Flink+Kudu, and DorisDB, and outlining the system design, data flow, scheduling, monitoring, and the resulting business and cost benefits.
This article details Xiaohongshu’s multi‑stage evolution of its OLAP infrastructure—from Redshift to Presto, ClickHouse, and finally DorisDB—describing the data pipeline, tool comparisons, advertising use‑case implementation, and the resulting performance and operational benefits.
This article details 58 Group's comprehensive evaluation of DorisDB, TiFlash, and ClickHouse for large‑scale analytical workloads, covering functional and performance benchmarks, real‑world use cases such as security analysis and DBA operations, data ingestion methods, cluster architecture, automation practices, and lessons learned.
The article describes how Yuanfudao's data middle platform built a high‑performance OLAP service using the MPP HOLAP engine DorisDB to unify real‑time and batch analytics, meet low‑latency and high‑concurrency requirements, and support diverse education‑industry use cases such as live‑stream monitoring, advertising, and order analytics.
Yuanfudao's data middle platform leverages the MPP database DorisDB to create a unified OLAP system that supports both real‑time and batch analytics, handling millions of queries daily with sub‑second latency while meeting complex business requirements across its education services.
This article details how KuJiaLe's big data team replaced their legacy ADB and Presto clusters with a DorisDB MPP database, achieving sub‑second query latency, unified real‑time and offline analytics, simplified ETL pipelines, and significant cost savings while supporting billion‑row tables and high‑QPS workloads.