This article details how Xingyun Retail Credit transitioned from traditional data warehouses to an Apache Doris‑based OLAP solution, covering data demand generation, OLAP engine selection challenges, multi‑stage implementation, performance optimizations, data‑warehouse construction, real‑world use cases, and future roadmap.
The Cloud Vision TV app implements a session‑id and placement‑id driven tracking pipeline that generates, collects, and processes lifecycle data across server and client layers, enabling fine‑grained engagement strategies, scene reconstruction via AC automata, and actionable BI dashboards to improve user retention and personalization.
This article presents HuoLala's practical approach to guaranteeing the stability of the Doris OLAP engine within its large‑scale big data platform, covering background, challenges, case studies, capability building, process standards, and future planning.
Facing massive daily query loads, Wanwu Newborn’s Watcher reporting platform migrated from MySQL, Greenplum, and Trino to StarRocks, cutting compute nodes by half while achieving 3‑10× faster query performance, higher success rates, and lower cost, as demonstrated by TPC‑DS and real‑world business query benchmarks.
The article reviews Doris 2.0's major enhancements—including point‑query concurrency, log‑analysis capabilities, cold‑hot data separation, lakehouse integration, and various performance and usability upgrades—explaining how these changes benefit OLAP workloads and simplify data‑engineering pipelines.
This article details Lenovo's implementation of ClickHouse in a manufacturing environment, covering the current data landscape, cluster architecture, integration challenges, performance enhancements, and solutions such as Seatunnel and query pre‑aggregation, illustrating how OLAP engines can address real‑time analytics and concurrency issues in production data pipelines.
The article examines Doris‑based real‑time data warehousing, outlining why teams choose this approach, comparing its low‑threshold development and operational simplicity to Flink’s high‑cost streaming, and highlighting latency, scale limits, and the strict monitoring required for production use.
This article presents an in‑depth overview of MatrixOne, an open‑source hyper‑converged cloud‑native database, detailing its three‑tier architecture of compute, data and file services, and explains the design and implementation of its OLAP engine, including parser, planner, optimizer, and push‑based execution pipeline.
This article details Xiaomi Group’s adoption of Apache Doris for OLAP, covering the evolution of their system selection, the architecture of their data ecosystem, practical implementations for user behavior analysis, and future plans to enhance performance, stability, and scalability.
This article describes how Financial One Account, a technology service arm of Ping An, migrated from a Hadoop‑Presto‑Kylin stack to an Apache Doris‑based data platform, detailing the architectural evolution, OLAP engine selection, metric system design, performance optimizations, and future roadmap for real‑time analytics.
This article explains how Ant Group tackles the performance challenges of large‑scale OLAP tables by using ADM to reduce data volume, employing Cube pre‑aggregation for reporting, and applying statistical sampling for exploratory analysis, detailing the processes, metrics, and architectural designs involved.
This article introduces Volcano EMR's cloud‑native OLAP solution, detailing its product overview, storage‑compute separation, elastic scaling, cost and hot‑cold data management, intelligent query analysis, multiple customer case studies, and future roadmap for real‑time and offline data warehousing.
This article examines the massive‑scale data requirements of an activity‑tracking platform, compares MySQL, Elasticsearch and HBase, and explains why ClickHouse—with its columnar storage, MergeTree engine, vectorized execution, and distributed architecture—offers the best combination of storage capacity, write performance, real‑time analysis, and query speed for billions of records.
This guide walks you through creating a fully managed EMR Serverless StarRocks instance, loading historical World Cup data, and running OLAP SQL queries to analyze championship counts and host‑nation performance, all using a free trial of compute and storage resources.
Apache Doris is an open‑source MPP OLAP database that combines a minimalist architecture, ease of use, rich features such as partition‑bucket pruning, materialized views, and bitmap indexes, and provides high‑performance, scalable, and reliable data warehousing for big‑data analytics.
The article explains the fundamental differences between OLTP (online transaction processing) and OLAP (online analytical processing), describes how ETL bridges the two, and provides a detailed side‑by‑side comparison of their characteristics, purposes, and design considerations.
OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are two distinct data processing systems—OLAP optimized for complex, multidimensional analysis and business intelligence, while OLTP handles high‑volume, real‑time transactional workloads—each serving different purposes, and often used together to drive data‑driven decision making.
This article details iQIYI's adoption of Iceberg for its data lake, covering the OLAP architecture, reasons for a data lake, Iceberg's table format advantages over Hive, platform construction, streaming ingestion, query and performance optimizations, real‑world business deployments, and future plans.
Tongcheng Travel migrated its multi‑stage OLAP platform from Druid/Kylin and ClickHouse‑Greenplum to a unified StarRocks solution, dramatically improving real‑time query latency, offline report performance, and CDP data processing while reducing operational complexity and enabling cloud‑native deployment.
The DataFunCon conference showcases leading experts from Ctrip, Didi, Bilibili, and ByteDance presenting next‑generation OLAP technologies such as StarRocks, ClickHouse, and ByteHouse, covering architecture, materialized views, ELT practices, and performance optimization to guide practitioners in big‑data platform selection and implementation.
Using the TPC‑DS benchmark’s 99 queries on a 1 TB dataset, this study evaluates the performance of four open‑source OLAP engines—ClickHouse, Doris, Presto, and ByConity—across basic, join, aggregation, subquery, and window‑function scenarios, revealing ByConity’s superior speed and the limitations of ClickHouse.
This article details Bilibili's technical practice of constructing a millisecond‑response lake‑warehouse platform using Apache Iceberg, covering the background challenges, unified architecture, multi‑dimensional sorting and indexing for query acceleration, the Magnus service for intelligent optimization, and the current production deployment and performance metrics.
This article details the three‑stage evolution of 360's real‑time data warehouse—from Storm + Druid + MySQL to Flink + Druid + TiDB and finally to Flink + Apache Doris—explaining architectural pain points, the reasons for choosing Doris, and how the new system delivers sub‑second query latency, strong consistency, and simplified operations across advertising scenarios.
This article details Tongcheng Travel’s production deployment of the StarRocks OLAP database, covering background, business scenarios, technical evaluation against ClickHouse and Greenplum, implementation with Flink SQL, real‑time analytics, offline reporting, CDP use cases, performance optimizations, and future cloud‑native plans.
This article details Waterdrop's transition from ClickHouse and TiDB to a unified StarRocks‑based OLAP platform on Alibaba Cloud EMR, covering architecture evolution, performance benchmarks, best‑practice recommendations, and future plans for large‑scale real‑time analytics.
This article details Dingdong Maicai’s business‑driven requirements for a real‑time OLAP platform, evaluates Apache Doris versus ClickHouse, describes the end‑to‑end architecture—including data ingestion, modeling, and query optimization techniques such as colocate joins, array_contains, broker load, bitmap, prefix and bloom‑filter indexes, and materialized views—and shares practical performance experiences and best‑practice recommendations.
This article explains how SQL, built on relational algebra, became the standard analysis language for OLAP engines, covering its history, data models, syntax, functions, aggregation techniques, window functions, subqueries, and practical optimization considerations for modern data warehouses.
This article explains what ClickHouse is, why a large MySQL table caused multi‑minute queries, and how migrating the data to ClickHouse using Docker, CREATE TABLE AS SELECT, and a lightweight sync tool reduced query time to under one second, while highlighting pitfalls and performance reasons.
This article introduces ClickHouse as an OLAP columnar database, explains its advantages over row‑based systems, details a real‑world migration from MySQL, shows performance benchmarks, shares practical installation and data‑sync methods, and highlights common pitfalls and solutions.
At Bilibili, the OLAP platform evolved through three phases—consolidating data services onto ClickHouse, migrating text search to ClickHouse, and integrating a lake‑house architecture—delivering massive cost reductions, sub‑second query latency, and scalable analytics for billions of daily events.
This article details Dingdong Maicai's adoption of Apache Doris as a real‑time OLAP engine, covering business requirements, comparative evaluation with ClickHouse, system architecture, practical applications such as real‑time analytics, B‑end queries, tag systems, and performance‑boosting techniques like Colocate Join, bitmap, prefix and Bloom‑filter indexes, materialized views, and streamlined Broker Load workflows.
This article presents a comprehensive interview with several big‑data platform experts, outlining the core components such as data integration, storage and computation, distributed scheduling, and query analysis, while also highlighting current challenges, best‑practice tools, and future trends in big‑data architecture.
This article explains why disaster‑recovery and active‑active architectures are essential for OLAP MPP data‑warehouse clusters, outlines the specific RPO/RTO requirements for batch and real‑time workloads, and compares several data‑synchronization techniques and active‑active deployment models with their advantages and drawbacks.
Dolphin, Alibaba’s hyper‑converged multi‑modal big‑data engine, unifies OLAP, AI, streaming, and batch workloads on a decoupled compute‑storage MPP foundation, offering a Dolphin SQL layer, advanced bitmap/GroupTable/AFile indexes, intelligent materialization, and one‑write‑multiple‑read storage that cuts costs over 70% while delivering sub‑millisecond queries on trillion‑row datasets.
This article presents ZhongAn Insurance's experience building real-time multi-dimensional analytics, covering application scenarios, technical challenges, the evolution of their architecture from offline to Flink‑ClickHouse and finally to StarRocks, and the principles guiding their technology selection.
This article details how ZuanZuan leveraged ClickHouse as the core OLAP engine for its massive self‑service analytics platform, covering OLAP engine selection criteria, system architecture, real‑world use cases, performance tuning, operational challenges, and future development plans.
Facing massive, multi‑source traffic and the need for instant analytics, Kuaigou’s real‑time data warehouse evolved from Spark on‑premise to a cloud‑native stack using Alibaba Blink, Flink, and layered OLAP models, streamlining development, cutting costs, and enabling diverse real‑time applications.
This article presents Youzu Network’s adoption of StarRocks for multi-dimensional analytics, detailing the historical OLAP challenges, StarRocks’ features and advantages, its application scenarios, data modeling choices, ingestion methods, performance benchmarks, and future roadmap for unified analytics.
This article introduces OLAP concepts, compares major OLAP solutions such as Druid, Kylin, Doris, and ClickHouse, outlines their features and suitable scenarios, and shares practical optimization techniques—including materialized views, caching, node tiering, and query tuning—to improve performance for high‑concurrency analytical workloads.
The Doris Summit 2022 recap outlines a ten‑year journey from an internal Baidu project to a top‑level Apache OLAP database, detailing explosive community growth, 2022 milestones, major feature releases up to version 1.2, and an ambitious 2023 roadmap focused on performance, lakehouse integration, multi‑modal analysis, cost efficiency, and enhanced usability.
This article introduces OLAP concepts, compares major OLAP engines such as Druid, Kylin, Doris, and ClickHouse, outlines real‑world application scenarios, and provides detailed optimization techniques—including materialized views, caching, tiered storage, and skip‑index configurations—to improve query performance.
This article explains why ClickHouse delivers high query speed by detailing storage‑engine optimizations such as pre‑sorting, columnar layout and compression, and compute‑engine techniques like vectorized execution, built‑in functions and minimal join usage, while also promoting the related book and giveaway.
This article introduces OLAP concepts, compares ROLAP, MOLAP, and HOLAP, evaluates mainstream OLAP engines such as Druid, Kylin, Doris, and ClickHouse, and presents practical optimization techniques including materialized views, caching, tiered storage, and query tuning for large‑scale analytical workloads.
This talk presents JD Retail’s big‑data OLAP solution, covering the massive, variable and complex traffic data challenges, the custom data‑ingestion and versioned update tools, ClickHouse query‑architecture upgrades, optimization techniques, and future plans for multi‑cluster querying and pre‑computation.
This article provides a comprehensive overview of OLAP technology evolution, covering its origins, modern requirements for massive and real‑time data, detailed comparisons of major open‑source OLAP engines such as Druid, Elasticsearch, Kylin, Doris/StarRocks, and ClickHouse, core architectural and storage techniques, and emerging trends like federated queries, hybrid storage, and lakehouse integration.
DataFun interviewed leading big‑data experts to outline the core components of modern big‑data platform architectures, discuss integration, storage, computation, scheduling, and query technologies, and share their perspectives on current challenges and future cloud‑native trends.
Facing massive daily data volumes and complex, ad‑hoc analytical needs, Zhaozhuan’s engineering team evaluated multiple OLAP engines and chose ClickHouse, then built a four‑layer self‑service analytics platform, detailing architecture, use‑cases, performance tuning, large‑scale joins, and future roadmap challenges.
This article examines why ClickHouse was chosen as the OLAP engine for a massive self‑service analytics platform, describes the system architecture, shares concrete memory and performance tuning parameters, and outlines current challenges and future roadmap for large‑scale real‑time data analysis.
This article examines the selection of ClickHouse as the OLAP engine for a self‑service analytics platform, describes the platform’s architecture, details memory and performance tuning techniques, discusses large‑scale join handling, and outlines current challenges and future development directions for ClickHouse.
This article introduces Apache Doris, an open‑source OLAP database, outlines its development history, typical application scenarios, key architectural components and performance optimizations, and then discusses the commercial Palo product and the planned future enhancements for both platforms.
The article presents a comprehensive risk insight platform built on ClickHouse, Flink, and intelligent algorithms, detailing its architecture, technical challenges, solutions, real-time data modeling, practical applications in fraud detection and user behavior analysis, and future optimization directions.
The article describes how TCL leveraged StarRocks to create a unified data analytics platform, detailing the company’s background, OLAP evolution, typical StarRocks use cases such as real‑time dashboards, HR analytics, and email alerts, and outlines future plans for further integration and performance improvements.
This article explains the differences between row-based and column-based storage, comparing their write and read performance, outlining advantages and disadvantages, and describing suitable scenarios such as OLAP queries, column families, compression, and indexing, to help choose the appropriate storage model.
This article reviews the practical applications and experimental explorations of StarRocks at 360, describing the cloud‑native lake‑warehouse product Yunzhou, its three‑tier architecture, performance comparisons with Trino using TPCH 100 GB, challenges of Kubernetes integration, and future directions for storage‑compute separation.
Cross‑Express consolidated multiple big‑data engines into a unified, high‑performance analytics platform using StarRocks, achieving millisecond‑level query latency, real‑time data warehousing, significant cost savings, and improved multi‑scenario business applications; the initiative also simplified BI development, reduced hardware requirements, and set a roadmap for future engine enhancements.
This article details why 360 selected StarRocks as its OLAP engine, compares its performance and resource usage against MySQL, Hive, Spark, Druid, ClickHouse and Doris, and describes the concrete deployment scenarios and data products built on StarRocks within the company.
This article details YiPay's big data BI analysis platform construction, covering its financial data use cases, platform architecture, OLAP engine implementations with ClickHouse, Presto, and Kylin, as well as identified challenges and future development directions.
This article details Huolala's journey from a MySQL‑centric analytics pipeline to a multi‑engine OLAP platform built on Doris, covering system architecture, data flow, stage‑wise evolution, engine selection, POC validation, performance tuning, stability measures, and future roadmap for self‑service analytics.
This case study describes how Noah Holdings migrated its self‑built CDH data platform to Alibaba Cloud, replacing Impala with Hologres to achieve faster, lower‑cost, and unified OLAP analytics that support real‑time business insights and improve overall data operations.
Facing soaring data volumes and performance bottlenecks, Noah Wealth replaced its self‑built CDH cluster with Alibaba Cloud’s unified big‑data platform and Hologres, streamlining OLAP analysis, cutting costs, accelerating queries to sub‑second response times, and enabling real‑time, multi‑dimensional analytics for its financial services.
This article explains the motivation, design, and implementation of vectorizing Apache Doris's storage layer using SIMD techniques, covering engine overview, vectorized programming concepts, storage architecture, index and predicate optimizations, delayed materialization, output improvements, and performance test results.
The article explains how ByteHouse extends ClickHouse with a full‑featured query optimizer—including rule‑based and cost‑based techniques, analyzer modules, plan construction, and distributed optimization—to overcome ClickHouse limitations and achieve significant performance gains on complex OLAP workloads.
This article presents a comprehensive overview of using Flink with Apache Hudi to build streaming data lake solutions, covering Hudi's background, core features, Flink‑Hudi integration design, practical use cases, recent roadmap updates, and a Q&A session.
This article explains how Apache Druid builds and queries bitmap indexes for efficient OLAP analysis, and describes a dictionary‑encoding plus bitmap solution—adapted from Kuaishou—to achieve exact deduplication even on high‑cardinality dimensions.
This technical presentation details NIO's evolution of OLAP solutions—from Druid and TiDB to Doris—explaining the selection criteria, Doris's advantages as a unified OLAP warehouse, its role in the CDP platform, practical deployment experiences, and lessons learned from real‑world usage.
This article details why 360 chose StarRocks as its OLAP engine, presents performance and operational comparisons with MySQL, Hive, Spark, Druid, Doris and ClickHouse, describes three major production use cases, and outlines ongoing explorations such as cloud‑native integration and Kubernetes support.
The article details how 37 Mobile Games built and continuously evolved a multi-dimensional analytics platform—covering business background, data challenges, the migration from MySQL through Druid, Impala, ClickHouse to StarRocks, self‑service data tools, monitoring, and future roadmap—highlighting technical decisions and lessons learned.
This presentation details Huolala's journey from its initial OLAP architecture to a multi‑engine platform, describing background, data‑flow layers, technical research, engine selection (Druid, ClickHouse, Doris), POC validation, performance tuning, stability measures, production rollout, problem analysis, and future roadmap.
This case study details how an online education platform migrated its real‑time data warehouse from Kudu to Alibaba Cloud Hologres, overcoming technical bottlenecks, reducing operational costs by nearly a million dollars annually, and achieving higher throughput, lower latency, and easier maintenance across multiple business scenarios.
This article explains Doris's fundamental concepts such as rows, columns, tablets, and partitions, provides guidelines for column definition, partitioning and bucketing strategies, and details table creation syntax and property settings for optimal big‑data storage and query performance.
This article provides a comprehensive technical overview of StarRocks' query optimizer, covering its cascades/ORCA-inspired architecture, logical and physical plan transformations, cost modeling, statistics derivation, memo structure, task scheduling, and practical examples of join optimization in a distributed OLAP engine.
Huolala’s data‑engineer Yang Qiuji shares how the company’s OLAP platform progressed from Druid (OLAP 1.0) to ClickHouse (OLAP 2.0) and finally to Apache Doris (OLAP 3.0), detailing business drivers, technical evaluations, POC results, stability measures, and future roadmap.
This article provides a comprehensive overview of ClickHouse, covering its origins, core characteristics, and the five most important features introduced in 2021—including JIT acceleration, Lambda‑based UDFs, native window functions, zero‑copy replication for S3/HDFS, and the Projection mechanism—highlighting why it remains a leading high‑performance OLAP database for big‑data analytics.
37 Mobile Games, a leading mobile game publisher, migrated its user‑profile analytics from a Hadoop‑Hudi‑Kafka‑Hive‑Flink stack to StarRocks, achieving sub‑second query latency on billion‑row tables, simplifying operations, reducing storage costs, and enabling real‑time data sync, as detailed in this technical case study.
Youzan replaced ClickHouse and Druid with Apache Doris, refined its vectorized engine by eliminating deserialization overhead in the merge‑aggregation phase, achieving roughly a 30 % query‑time boost, and validated compatibility through SQL rewriting and traffic replay, while planning further SIMD‑based optimizations and broader adoption.
This article explains how to extend the dble distributed middleware with ClickHouse to provide OLAP capabilities, covering environment setup, XML configuration of user.xml, db.xml and sharding.xml, command‑line testing, data import, and a sample architecture that combines dble, MySQL and ClickHouse.
The whitepaper “From ClickHouse to ByteHouse” details ByteDance’s large‑scale deployment of ClickHouse, the challenges of moving it to production, and the key optimizations ByteHouse introduces—including custom table engines, a revamped query optimizer, and elastic compute‑storage separation—to achieve petabyte‑level OLAP performance.
Apache Druid is a distributed, column‑store OLAP engine designed for massive real‑time data ingestion and sub‑second queries; this article explains its LSM‑tree‑inspired architecture, DataSource and Segment structures, memory‑based querying, practical deployment steps, common pitfalls, and optimization techniques for high‑throughput analytics.
The article traces the evolution of data platforms from early data warehouses—using schema‑on‑write, columnar storage, and MPP engines—to data lakes that retain raw data with schema‑on‑read, and finally to lakehouse architectures that merge storage and compute, offering unified metadata, versioning, and support for BI, big‑data, AI, and HPC workloads.
The Qunhe Technology data team replaced their legacy Hadoop and Presto clusters with a StarRocks MPP database, achieving up to three times faster queries, supporting billion‑row tables and sub‑second latency for both real‑time and analytical workloads on a daily 1TB data influx.
This article describes how the gaming company Boke City rebuilt its comprehensive data service platform by replacing a CDH‑based Impala solution with StarRocks, detailing the architectural changes, performance benchmark results, and the resulting improvements in query speed, real‑time data updates, and operational simplicity.
This article explains the fundamental differences between transactional (OLTP) and analytical (OLAP) database workloads, describes how data warehouses isolate analytical queries, and introduces star and snowflake schema designs for efficient reporting and business intelligence.
This guide explains ClickHouse fundamentals, OLAP versus OLTP concepts, columnar storage benefits, core performance techniques, the MergeTree family and its indexing, specialized table engines, installation on Linux, Docker deployment, and integration with HDFS, MySQL, and Kafka for modern analytical workloads.
This article explains how to construct a real‑time data warehouse by combining Flink CDC for end‑to‑end change data capture with StarRocks' high‑performance OLAP engine, detailing the architectural challenges, optimization techniques, and a practical e‑commerce case study.
This article details JD Retail's OLAP platform construction, covering control plane design, architecture, business and operation management, real‑time data updates, materialized view usage, join optimizations, high‑concurrency and high‑throughput scenarios, and promotional preparation strategies, illustrated with diagrams and performance metrics.
This article explores how combining Flink CDC with StarRocks can streamline real‑time data pipelines, reduce component complexity, support both full and incremental synchronization, and enable efficient OLAP queries and updates for fast, scalable analytics across diverse business scenarios.
Apache Kylin leverages pre‑computed OLAP cubes on Hadoop/Spark/Flink to deliver sub‑second query responses for massive datasets, detailing its architecture, integration with BI platforms, user security, cube building, monitoring, and storage using HBase, illustrating how it overcomes big‑data analytical challenges.
This article presents JD Retail's OLAP platform construction and practical scenarios, covering control‑plane design, architecture, business management, operational safeguards, real‑time data updates, materialized view acceleration, join optimization techniques, high‑concurrency queries, and large‑scale write throughput for e‑commerce peak periods.
The article explains the challenges of big‑data analytics, introduces pre‑computation OLAP concepts, and details how Apache Kylin together with Spark, Flink, Presto and other components can be integrated into a BI platform to achieve near‑real‑time query performance on massive datasets.
During the 2022 China Internet Industry Application Salon, JD Cloud’s product manager explained how the Color gateway, an API gateway handling billions of daily requests, overcomes stability, high‑availability, reliability, and performance challenges during peak sales by adopting a cloud‑native ClickHouse data warehouse that boosts processing speed, reduces costs, and provides real‑time analytics.
This article explains the design and implementation of StarRocks 2.x Primary Key tables, covering real‑time update mechanisms, write and commit workflows, in‑memory primary indexing, compaction, read‑path optimizations, performance benchmarks, and upcoming features such as partial and conditional updates.
Facing massive data storage and query challenges, 360 upgraded its analytics architecture by adopting StarRocks, achieving multi‑dimensional, high‑concurrency analysis, simplified data pipelines, and significant performance and cost improvements across its radar and user‑portrait platforms.
The article details ByteDance's large‑scale deployment of the Presto OLAP engine for ad‑hoc, BI, and near‑real‑time analytics, describing its architecture, multi‑coordinator high‑availability design, routing gateway, adaptive cancel, history server, materialized‑view support, Hudi connector integration, and how these innovations improve performance, stability, and operational efficiency.
This article provides a comprehensive overview of ClickHouse, covering its core use cases, detailed explanations of the various table engines, recommended schema and deployment patterns, performance‑tuning parameters, tooling choices, and operational guidelines for building and maintaining high‑availability OLAP clusters.
This article provides a comprehensive overview of ClickHouse, an open‑source column‑oriented MPP analytical database, covering its advantages and drawbacks, key features, typical use cases, data access flow, installation steps, core directories, indexes, data types, database and table engines, as well as detailed cluster architecture and deployment patterns.
This article describes how ZhongAn Insurance’s JiZhi data‑analysis platform migrated from ClickHouse to the MPP OLAP engine StarRocks, detailing the business requirements, architectural challenges, benchmark results across single‑table and multi‑table queries, and the resulting improvements in latency, concurrency, and operational simplicity for real‑time analytics.
This article presents a technical case study of ZhongAn's Jizhi analytics platform, detailing how switching from ClickHouse to the MPP OLAP engine StarRocks resolved multi‑concurrency and join performance bottlenecks, improved real‑time query speed, and enabled near‑billion‑row data handling for insurance business operations.
After months of using MongoDB for frontend monitoring logs, the data grew to billions of records causing severe query slowdown, prompting a migration to ClickHouse where columnar storage, partitioning, and OLAP capabilities dramatically improved performance and storage efficiency.
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.
Facing inflexible point‑lookup limits and slow query times on HBase, Zepp Health redesigned its massive event‑tracking data pipeline—migrating ingestion through Kafka, Flink, and Hudi to a StarRocks‑based OLAP layer—achieving sub‑100 ms average query latency, 20 % storage savings, and dramatically faster multi‑dimensional analytics.
