This article provides a comprehensive overview of data warehouse implementation, covering DDL‑based creation, the role of ETL processes, the functions of OLAP/BI tools, data cube operations, and the trade‑offs among MOLAP, ROLAP, and HOLAP architectures.
This article explains OLAP as a core data‑warehouse component that enables fast, multidimensional analysis for business intelligence, describes the structure of OLAP cubes, outlines basic operations such as drill‑down, roll‑up, slice‑and‑dice and pivot, compares MOLAP, ROLAP and HOLAP, and discusses OLAP’s relationship with OLTP and cloud architectures.
This article explains the classification of OLAP data warehouses by data volume and modeling approach, compares MOLAP, ROLAP, HOLAP and HTAP, reviews popular open‑source ROLAP systems, and details advanced performance‑boosting techniques such as MPP architectures, cost‑based optimization, vectorized execution, dynamic code generation, and runtime filtering.
This article examines Meituan’s data warehouse evolution, detailing the limitations of MOLAP with Kylin, the adoption of Doris‑driven ROLAP using MPP technology, and the practical optimizations—such as join predicate pushdown, concurrent execution, colocate join, and bitmap aggregation—that improve real‑time analytics and reduce costs.
This article provides a comprehensive overview of OLAP systems, classifying them by data volume and modeling approach, comparing MOLAP, ROLAP, HOLAP and HTAP, reviewing popular open‑source products, and detailing architectural, query‑optimization, vectorization, storage and resource‑management techniques that affect analytical warehouse performance.
The article details Meituan's food‑delivery data warehouse transformation from a MOLAP‑centric design to a dual‑engine (MOLAP + ROLAP) architecture powered by Apache Doris, describing the challenges of massive, mutable data, the technical trade‑offs, and the performance gains achieved through MPP, predicate push‑down, multi‑instance concurrency, colocate joins, and bitmap aggregation.
Meituan Takeaway’s data warehouse combines Apache Kylin’s MOLAP cubes for stable dimensions with Apache Doris’s MPP‑driven ROLAP engine to handle changing dimensions, detail queries, and near‑real‑time analytics, achieving millisecond‑level responses, reduced storage/compute costs, and simplifying operations across diverse analytical workloads.
