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OLAP

408 articles · Page 4 of 5
Tencent Cloud Developer
Tencent Cloud Developer
Oct 20, 2020 · Databases

ClickHouse: Architecture, Core Features, and Limitations for Interactive Analytics

ClickHouse is a PB‑scale, open‑source columnar OLAP database that uses a ZooKeeper‑coordinated sharded cluster, columnar storage, vectorized execution, advanced compression, data‑skipping indexes, and materialized views to deliver high‑performance interactive analytics, yet it requires manual shard management, lacks a mature MPP optimizer, and handles real‑time single‑row writes poorly.

ClickHouseColumnar StorageMaterialized Views
0 likes · 18 min read
ClickHouse: Architecture, Core Features, and Limitations for Interactive Analytics
DataFunTalk
DataFunTalk
Oct 19, 2020 · Big Data

Impala Optimization and Practices at NetEase Big Data Platform

This article presents a comprehensive overview of NetEase's use of Impala as an OLAP query engine, detailing its architectural advantages, performance benefits, enhancements such as management servers, metadata synchronization, high‑availability via Zookeeper, expanded storage support, and real‑world deployment cases in the "Mammoth" platform and NetEase Cloud Music.

High AvailabilityImpalaMetadata Sync
0 likes · 11 min read
Impala Optimization and Practices at NetEase Big Data Platform
Big Data Technology & Architecture
Big Data Technology & Architecture
Oct 15, 2020 · Big Data

Meituan's OLAP Requirements and Apache Kylin Deployment: Architecture, Challenges, and Comparative Analysis

This article describes Meituan's massive OLAP workloads, the specific challenges of data scale, complex schemas, and precise counting, explains how Apache Kylin was integrated using wide tables and bitmap deduplication, compares its performance and features with Presto, Druid and other engines, and outlines future improvements.

Apache KylinBig DataData Warehouse
0 likes · 19 min read
Meituan's OLAP Requirements and Apache Kylin Deployment: Architecture, Challenges, and Comparative Analysis
dbaplus Community
dbaplus Community
Oct 13, 2020 · Big Data

How to Build a Real‑Time Data Warehouse with Flink: Principles, Architecture, and Best Practices

This article explains why real‑time data warehouses are needed, outlines their core principles, compares them with offline warehouses, describes typical use cases such as real‑time OLAP, dashboards, feature generation and monitoring, and provides a step‑by‑step guide to designing, implementing, and operating a Flink‑based streaming warehouse with Kafka, HBase, and metadata management.

Data EngineeringFlinkOLAP
0 likes · 29 min read
How to Build a Real‑Time Data Warehouse with Flink: Principles, Architecture, and Best Practices
ITPUB
ITPUB
Oct 12, 2020 · Databases

Why ClickHouse Outperforms Other Databases: Core Features Unveiled

This article explains how ClickHouse’s column‑oriented storage, vectorized execution engine, rich DBMS capabilities, flexible table engines, and carefully designed distributed architecture enable it to handle massive workloads with sub‑second query latency, making it a standout OLAP solution.

ClickHouseColumnar DatabaseOLAP
0 likes · 29 min read
Why ClickHouse Outperforms Other Databases: Core Features Unveiled
Architecture Digest
Architecture Digest
Oct 5, 2020 · Databases

ClickHouse Overview, MySQL Migration, Performance Benchmark, and Practical Tips

This article introduces ClickHouse as an OLAP columnar database, explains the differences between row‑ and column‑oriented storage, details a real‑world migration from MySQL using a CREATE TABLE AS SELECT approach, presents performance comparisons, discusses synchronization methods, and shares common pitfalls and solutions.

ClickHouseColumnar DatabaseOLAP
0 likes · 6 min read
ClickHouse Overview, MySQL Migration, Performance Benchmark, and Practical Tips
Big Data Technology Architecture
Big Data Technology Architecture
Sep 30, 2020 · Databases

Core Technologies of OLAP Systems: Storage, Computation, Optimizer, and Emerging Trends

This article systematically examines the core technologies of OLAP systems, covering storage models, columnar formats, indexing, distributed storage architectures, query execution steps, optimizer designs, and emerging trends such as real‑time analytics, HTAP, cloud‑native deployment, and hardware acceleration.

Columnar StorageOLAPQuery Optimizer
0 likes · 23 min read
Core Technologies of OLAP Systems: Storage, Computation, Optimizer, and Emerging Trends
DataFunTalk
DataFunTalk
Sep 28, 2020 · Databases

Understanding OLAP Types, Open‑Source Products, and Performance Optimization Techniques

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.

Data WarehouseMOLAPOLAP
0 likes · 27 min read
Understanding OLAP Types, Open‑Source Products, and Performance Optimization Techniques
Top Architect
Top Architect
Sep 25, 2020 · Databases

Introduction to ClickHouse: Installation, MySQL Migration, Performance Testing, and Best Practices

This article introduces ClickHouse, a column‑store OLAP database, explains its advantages over row‑store systems, guides installation on macOS, details migration strategies from MySQL, presents performance benchmarks showing 200‑fold speedups, and discusses synchronization methods, pitfalls, and practical tips.

ClickHouseColumnar DatabaseData Migration
0 likes · 6 min read
Introduction to ClickHouse: Installation, MySQL Migration, Performance Testing, and Best Practices
DataFunTalk
DataFunTalk
Sep 24, 2020 · Databases

Understanding OLAP vs. OLTP and the Fundamentals of Data Warehousing

This article explains the core differences between OLTP and OLAP, evaluates whether traditional OLTP databases like MySQL can handle analytical workloads, introduces benchmark queries, and provides a comprehensive overview of data‑warehouse concepts such as data sources, fact and dimension tables, multi‑dimensional modeling, and common cube operations.

AnalyticsData WarehouseHTAP
0 likes · 21 min read
Understanding OLAP vs. OLTP and the Fundamentals of Data Warehousing
Tencent Cloud Developer
Tencent Cloud Developer
Sep 22, 2020 · Big Data

Evolution and Architecture of Beike's OLAP Platform: From Hive/MySQL to Multi‑Engine Flexibility

Beike’s OLAP platform has progressed from a basic Hive‑MySQL batch pipeline to a Kylin‑based single‑engine solution, and now to a flexible multi‑engine architecture that uses a query‑engine layer to route metrics across Kylin, Druid, ClickHouse and Doris, dramatically cutting cube‑build times, supporting real‑time ingestion, and paving the way for further engine consolidation and automated performance routing.

Apache DruidApache KylinBeike
0 likes · 17 min read
Evolution and Architecture of Beike's OLAP Platform: From Hive/MySQL to Multi‑Engine Flexibility
Architect
Architect
Sep 19, 2020 · Databases

ClickHouse Overview, MySQL Migration, Performance Testing, and Practical Tips

This article introduces ClickHouse as a high‑performance columnar OLAP database, compares it with row‑based systems, details migration methods from MySQL, presents performance benchmarks showing dramatic speed gains, discusses data synchronization strategies, explains why ClickHouse is fast, and shares common pitfalls and solutions.

ClickHouseColumnar DatabaseData Migration
0 likes · 5 min read
ClickHouse Overview, MySQL Migration, Performance Testing, and Practical Tips
Tencent Cloud Developer
Tencent Cloud Developer
Sep 9, 2020 · Big Data

Tencent Game Marketing Deduplication Service: Technical Evolution from TDW to ClickHouse

Tencent’s game marketing analysis system “EAS” evolved from inefficient TDW HiveSQL jobs and file‑heavy real‑time pipelines to a scalable ClickHouse‑based deduplication service that processes hundreds of thousands of daily activity counts in sub‑second time, offering fast, reliable, and maintainable participant deduplication for massive marketing campaigns.

ClickHouseDeduplicationLevelDB
0 likes · 10 min read
Tencent Game Marketing Deduplication Service: Technical Evolution from TDW to ClickHouse
DataFunTalk
DataFunTalk
Sep 7, 2020 · Big Data

Real‑time Data Warehouse Architecture and Best Practices in Alibaba Search Recommendation

This article presents Alibaba's search‑recommendation real‑time data warehouse, describing its business background, typical use cases, key requirements, the evolution from architecture 1.0 to 2.0 with Flink and Hologres, best‑practice patterns such as row/column storage, stream‑batch integration, high‑concurrency updates, and future directions like real‑time joins and persistent dimension storage.

Big DataFlinkHologres
0 likes · 13 min read
Real‑time Data Warehouse Architecture and Best Practices in Alibaba Search Recommendation
Big Data Technology & Architecture
Big Data Technology & Architecture
Aug 30, 2020 · Big Data

Kylin Cube Construction Principles and Optimization Techniques

This article explains the fundamentals of Kylin Cube construction—including dimensions, measures, Cuboid generation, layer-by-layer and in‑memory building algorithms, storage mechanisms, and various optimization strategies such as derived dimensions, aggregation groups, row‑key design, and concurrency granularity—providing a comprehensive guide for big‑data OLAP practitioners.

AggregationBig DataCube
0 likes · 14 min read
Kylin Cube Construction Principles and Optimization Techniques
Aikesheng Open Source Community
Aikesheng Open Source Community
Aug 13, 2020 · Databases

Introduction to ClickHouse: Features, Installation, Performance Testing, and Comparison

This article introduces ClickHouse, an open‑source column‑oriented OLAP database, detailing its key features, appropriate use cases, installation steps, performance benchmark queries, and how it compares with other columnar storage solutions while highlighting its adoption by major internet companies.

Big DataClickHouseColumnar Database
0 likes · 10 min read
Introduction to ClickHouse: Features, Installation, Performance Testing, and Comparison
Big Data Technology Architecture
Big Data Technology Architecture
Aug 12, 2020 · Databases

Core Features and Architecture of ClickHouse

ClickHouse, the high‑performance columnar OLAP DBMS behind Yandex.Metrica, combines complete DBMS capabilities, column‑oriented storage with compression, vectorized execution, flexible table engines, multi‑master clustering, and extensive SQL support, offering fast online queries and scalable distributed processing for massive data workloads.

ClickHouseColumnar DatabaseOLAP
0 likes · 28 min read
Core Features and Architecture of ClickHouse
Tencent Cloud Developer
Tencent Cloud Developer
Aug 6, 2020 · Big Data

ClickHouse Real‑Time Analytics at QQ Music: Challenges, Solutions, and Tencent Cloud Best Practices

QQ Music replaced its slow Hive warehouse with a massive ClickHouse cluster, achieving sub‑second to ten‑second query latency on petabyte‑scale data, enabling real‑time analytics for non‑technical users, and following five operational best practices—ZooKeeper planning, idempotent writes, sensible partitions, read‑write separation, and localized joins—while leveraging Tencent Cloud’s managed ClickHouse service.

ClickHouseOLAPPerformance Tuning
0 likes · 24 min read
ClickHouse Real‑Time Analytics at QQ Music: Challenges, Solutions, and Tencent Cloud Best Practices
Big Data Technology & Architecture
Big Data Technology & Architecture
Aug 5, 2020 · Big Data

An Introduction to Apache Kylin: Architecture, Core Concepts, Installation, and Enterprise Use Cases

This article provides a comprehensive overview of Apache Kylin, covering its background, core OLAP concepts, technical architecture, installation steps, cube-building methods, real‑world enterprise deployments, and resources for further learning, illustrating how it enables sub‑second query performance on massive datasets.

Apache KylinBig DataCube
0 likes · 20 min read
An Introduction to Apache Kylin: Architecture, Core Concepts, Installation, and Enterprise Use Cases
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Jul 31, 2020 · Big Data

Designing and Implementing a Comprehensive Data Metric System and Data Warehouse for Online Education

This article explains how to build a systematic data metric framework—including indicator definitions, dimension management, data dictionary, and data warehouse architecture—to turn massive online‑school data into actionable business insights and support efficient decision‑making and BI visualization.

Business IntelligenceIndicator DesignOLAP
0 likes · 23 min read
Designing and Implementing a Comprehensive Data Metric System and Data Warehouse for Online Education
dbaplus Community
dbaplus Community
Jul 21, 2020 · Databases

What Are the Different Types of OLAP and How Do They Impact Performance?

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.

Data WarehouseHTAPMOLAP
0 likes · 30 min read
What Are the Different Types of OLAP and How Do They Impact Performance?
DataFunTalk
DataFunTalk
Jul 18, 2020 · Databases

Core Features and Architecture of ClickHouse: An In‑Depth Overview

This article provides a comprehensive technical overview of ClickHouse, covering its complete DBMS capabilities, column‑oriented storage and compression, vectorized execution engine, relational SQL support, diverse table engines, multi‑master clustering, sharding, and the design philosophies that make it exceptionally fast for large‑scale analytical workloads.

ClickHouseColumnar DatabaseDatabase Architecture
0 likes · 29 min read
Core Features and Architecture of ClickHouse: An In‑Depth Overview
Big Data Technology Architecture
Big Data Technology Architecture
Jun 28, 2020 · Databases

Understanding OLAP Data Warehouse Types, Architectures, and Performance Optimizations

This article provides a comprehensive overview of OLAP data warehouses, covering classification by data volume and modeling, detailed explanations of MOLAP, ROLAP, HOLAP and HTAP, common open‑source implementations, and a deep dive into performance‑boosting techniques such as MPP architectures, cost‑based optimization, vectorized execution, dynamic code generation, storage compression, runtime filters and resource management.

Cost-Based OptimizationData WarehouseDynamic Code Generation
0 likes · 25 min read
Understanding OLAP Data Warehouse Types, Architectures, and Performance Optimizations
Suning Technology
Suning Technology
Jun 19, 2020 · Big Data

How Suning’s Big Data Engine Powered a Record‑Breaking 618 Sale

Suning’s 618 shopping festival showcased a massive sales surge backed by its big‑data platform, which processed over 200 billion requests, handled 38.5 PB of daily data, and delivered 31.5 trillion computations, while Kafka and HBase sustained tens of millions of TPS to ensure a seamless consumer experience.

618 SaleHBaseOLAP
0 likes · 5 min read
How Suning’s Big Data Engine Powered a Record‑Breaking 618 Sale
Big Data Technology Architecture
Big Data Technology Architecture
Jun 11, 2020 · Big Data

Kylin at Autohome: Development History, Deployment Practices, Optimizations, and Future Roadmap

This article details Autohome's use of Apache Kylin as its core OLAP engine, covering its architecture, large‑scale Cube deployment, real‑world business applications, a series of performance and operational optimizations, cluster upgrade experiences, and upcoming plans for real‑time OLAP and cloud‑native evolution.

Cloud NativeKylinOLAP
0 likes · 24 min read
Kylin at Autohome: Development History, Deployment Practices, Optimizations, and Future Roadmap
Big Data Technology Architecture
Big Data Technology Architecture
Jun 4, 2020 · Big Data

Building a Real-Time OLAP Analytics Platform for QQ Music with ClickHouse and Tencent Cloud EMR

QQ Music’s data team tackled massive PB‑scale, real‑time analytics challenges by migrating from Hive to a ClickHouse‑based OLAP platform integrated with Tencent Cloud EMR and Superset, achieving low‑latency, high‑availability data processing, self‑service visualization, and efficient read/write scaling for billions of daily events.

ClickHouseCloud EMRData Visualization
0 likes · 11 min read
Building a Real-Time OLAP Analytics Platform for QQ Music with ClickHouse and Tencent Cloud EMR
Tencent Cloud Developer
Tencent Cloud Developer
Jun 2, 2020 · Big Data

Real‑time OLAP Analytics for QQ Music Using ClickHouse and Tencent Cloud EMR

QQ Music’s new real‑time OLAP platform, built on ClickHouse, Superset and Tencent Cloud EMR, ingests petabyte‑scale streaming and batch data with SSD‑backed ZooKeeper, load‑balanced writes, optimized partitions and read/write separation, delivering second‑level query responses that are several times faster than Hive, Presto or SparkSQL and enabling self‑service BI for thousands of users.

Big DataClickHouseOLAP
0 likes · 12 min read
Real‑time OLAP Analytics for QQ Music Using ClickHouse and Tencent Cloud EMR
Big Data Technology Architecture
Big Data Technology Architecture
May 31, 2020 · Databases

An Overview of ClickHouse: Features, Performance, Use Cases, and Limitations

ClickHouse is a column‑oriented, open‑source OLAP database developed by Yandex that offers high‑compression columnar storage, vectorized execution, and massive read/write throughput, making it ideal for large‑scale analytics while having specific usage scenarios and notable limitations such as lack of true transactions and secondary indexes.

ClickHouseColumnar DatabaseOLAP
0 likes · 7 min read
An Overview of ClickHouse: Features, Performance, Use Cases, and Limitations
DataFunTalk
DataFunTalk
Apr 15, 2020 · Big Data

Apache Flink OLAP Engine: Architecture, Optimizations, and Use Cases

This article presents an in‑depth overview of Apache Flink's new OLAP engine, covering OLAP fundamentals, the three OLAP models, Flink's unified streaming‑batch‑OLAP architecture, performance optimizations, benchmark results, and future development directions.

Apache FlinkBig DataOLAP
0 likes · 11 min read
Apache Flink OLAP Engine: Architecture, Optimizations, and Use Cases
Big Data Technology Architecture
Big Data Technology Architecture
Mar 28, 2020 · Big Data

Apache Kylin: From Extreme OLAP Engine to an Analytical Data Warehouse for Big Data

The article chronicles Apache Kylin's evolution from an Apache incubator OLAP engine to a comprehensive analytical data warehouse, highlighting its five‑year growth, extensive enterprise adoption, core data‑warehouse features, and the community’s rebranding to better reflect its big‑data capabilities.

AnalyticsApache KylinData Warehouse
0 likes · 7 min read
Apache Kylin: From Extreme OLAP Engine to an Analytical Data Warehouse for Big Data
DataFunTalk
DataFunTalk
Jan 10, 2020 · Big Data

Design and Evolution of iQIYI's Real-Time Analytics Platform (RAP)

The article details iQIYI's Real-Time Analysis Platform (RAP), describing its motivation, architecture evolution from RAP 1.x to 2.x, OLAP engine selection, product design workflow, integration of Druid KIS and Flink, enhanced diagnostics, and real-world applications in membership monitoring, recommendation evaluation, and smart TV alerting.

DruidFlinkOLAP
0 likes · 12 min read
Design and Evolution of iQIYI's Real-Time Analytics Platform (RAP)
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 9, 2020 · Big Data

Design and Evolution of iQIYI Real-Time Analysis Platform (RAP)

iQIYI’s Real‑Time Analysis Platform (RAP) combines Apache Druid with Spark/Flink to deliver minute‑level, low‑latency multidimensional analytics via a web wizard, supporting hundreds of streaming tasks and thousands of reports across membership, recommendation, and TV monitoring, while simplifying development and maintenance.

Apache DruidBig DataFlink
0 likes · 13 min read
Design and Evolution of iQIYI Real-Time Analysis Platform (RAP)
Tongcheng Travel Technology Center
Tongcheng Travel Technology Center
Dec 31, 2019 · Big Data

Apache Kylin Overview and Model Optimization Practices for Trajectory Analytics

This article introduces Apache Kylin, details its deployment at Tongcheng Yilong, explains the design of a large‑scale trajectory model, and provides step‑by‑step optimization techniques—including cube dimension reduction, HBase rowkey tuning, build parameter tweaks, high‑cardinality handling, and query compression disabling—to achieve sub‑second OLAP queries on multi‑terabyte data.

Apache KylinBig DataCube
0 likes · 17 min read
Apache Kylin Overview and Model Optimization Practices for Trajectory Analytics
Hulu Beijing
Hulu Beijing
Oct 28, 2019 · Big Data

How Hulu Uses Big Data to Power Precise Advertising and Real‑Time Streaming

At a Tsinghua University forum, Hulu presented a comprehensive overview of its big‑data solutions for advertising and streaming, covering challenges of massive, complex data, the limits of MySQL, and advanced techniques using HBase, Protobuf, Redis batch pipelines, and its own MPP engine Nesto for high‑performance, scalable analytics.

AdvertisingHBaseMPP
0 likes · 6 min read
How Hulu Uses Big Data to Power Precise Advertising and Real‑Time Streaming
Big Data Technology Architecture
Big Data Technology Architecture
Oct 15, 2019 · Big Data

Introduction to Apache Kylin: A Fast Big Data OLAP Engine

Apache Kylin is an open‑source, Hadoop‑based OLAP engine that provides sub‑second, multi‑dimensional SQL queries on massive datasets, with features such as cube pre‑computation, real‑time analytics, and seamless BI tool integration, and its latest v2.6.4 release adds numerous fixes and improvements.

Apache KylinBI IntegrationHadoop
0 likes · 4 min read
Introduction to Apache Kylin: A Fast Big Data OLAP Engine
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Sep 27, 2019 · Big Data

Design Principles and Architecture of Apache Kylin for Sub‑Second OLAP Queries

This article explains how Apache Kylin, an open‑source distributed analytics engine built on Hadoop/Spark, achieves sub‑second OLAP query performance through pre‑computed cubes, a layered cuboid generation algorithm, bitmap‑based distinct counting, dimension optimization techniques, and tight integration with HBase for storage and fast SQL querying.

Apache KylinBig DataCube
0 likes · 15 min read
Design Principles and Architecture of Apache Kylin for Sub‑Second OLAP Queries
Big Data Technology & Architecture
Big Data Technology & Architecture
Sep 23, 2019 · Big Data

Applying Apache Kylin for Large‑Scale OLAP at Meituan: Architecture, Challenges, and Performance Evaluation

This article describes Meituan’s large‑scale OLAP requirements, how Apache Kylin was integrated to meet them, the architectural solutions, performance benchmarks against other engines, and future work, providing practical insights for building stable, precise, and high‑performance analytics platforms.

Apache KylinBig DataData Warehouse
0 likes · 20 min read
Applying Apache Kylin for Large‑Scale OLAP at Meituan: Architecture, Challenges, and Performance Evaluation
Big Data Technology & Architecture
Big Data Technology & Architecture
Sep 13, 2019 · Big Data

Data Warehouse Overview, Architecture, and Modeling Methodology

This article provides a comprehensive introduction to data warehouses, covering their definition, architectural layers, characteristics, modeling approaches such as Inmon and Kimball, fact and dimension table design, star and snowflake schemas, and best‑practice principles for building scalable, maintainable warehouse solutions in the big‑data ecosystem.

Database DesignETLOLAP
0 likes · 19 min read
Data Warehouse Overview, Architecture, and Modeling Methodology
Big Data Technology & Architecture
Big Data Technology & Architecture
Aug 29, 2019 · Databases

Differences Between OLTP and OLAP: Characteristics, Resource Requirements, and Use Cases

The article explains the two main types of data processing—OLTP for real‑time transactional workloads and OLAP for analytical data‑warehousing tasks—detailing their distinct characteristics, resource demands, typical features, major vendor products, and a five‑point comparison of their design and usage.

Data WarehousingDatabasesOLAP
0 likes · 6 min read
Differences Between OLTP and OLAP: Characteristics, Resource Requirements, and Use Cases
Big Data Technology Architecture
Big Data Technology Architecture
Aug 21, 2019 · Big Data

Key Big Data Terminology: Offline vs Real-time Computing, Real-time vs Ad Hoc Queries, OLTP vs OLAP, Row vs Column Storage

This article explains fundamental big‑data concepts by comparing offline (batch) and real‑time (stream) computing, distinguishing real‑time queries from ad‑hoc queries, clarifying OLTP versus OLAP workloads, and outlining the differences between row‑based and column‑based storage architectures.

Big DataColumn StorageOLAP
0 likes · 5 min read
Key Big Data Terminology: Offline vs Real-time Computing, Real-time vs Ad Hoc Queries, OLTP vs OLAP, Row vs Column Storage
Big Data Technology & Architecture
Big Data Technology & Architecture
Aug 8, 2019 · Big Data

Comprehensive Guide to Apache Kylin: Architecture, Concepts, Cube Design and Optimization

This article provides an in‑depth overview of Apache Kylin’s pre‑computation architecture, data‑warehouse concepts, step‑by‑step cube creation from Hive tables, and advanced optimization techniques such as derived dimensions, aggregation groups, and HBase row‑key encoding to achieve sub‑second OLAP queries on massive datasets.

Apache KylinBig DataCube
0 likes · 20 min read
Comprehensive Guide to Apache Kylin: Architecture, Concepts, Cube Design and Optimization
Big Data Technology & Architecture
Big Data Technology & Architecture
Jul 29, 2019 · Databases

Comprehensive Comparison of Apache Kylin and Apache Doris: Architecture, Data Models, Storage, Query, and Operations

This article provides an in‑depth technical comparison of Apache Kylin and Apache Doris, covering their system architectures, aggregation and detail data models, storage engines, data import processes, query execution, deduplication, metadata handling, performance, high availability, maintainability, usability, schema‑change capabilities, features, and community ecosystems.

Apache DorisApache KylinBig Data
0 likes · 21 min read
Comprehensive Comparison of Apache Kylin and Apache Doris: Architecture, Data Models, Storage, Query, and Operations
Tencent Cloud Developer
Tencent Cloud Developer
Jul 18, 2019 · Big Data

Tencent iData Analysis Center: Why We Chose Spark as Our Computing Platform

Tencent’s iData analysis center selected Spark as its new computing platform because, unlike ElasticSearch, TiDB, and other MPP solutions, Spark offers iterative processing, shuffle support, robust SQL and DAG scheduling, and flexible SMP‑style data exchange, enabling efficient OLAP on billions of game‑user records.

Big DataData PlatformDistributed Computing
0 likes · 13 min read
Tencent iData Analysis Center: Why We Chose Spark as Our Computing Platform
Tencent Cloud Developer
Tencent Cloud Developer
Jul 16, 2019 · Big Data

Design and Challenges of Tencent iData Analysis Center Backend: Bitmap Storage and MapReduce Architecture

Tencent’s iData Analysis Center rebuilt its backend as TGMars, replacing a rigid row‑oriented bitmap store and single‑node MapReduce pipeline with a more extensible architecture that shards user behavior bitmaps, eliminates shuffle overhead, and adds columnar storage, iterative processing and SQL‑like capabilities using Spark to overcome scalability and flexibility limitations.

MapReduceOLAPbitmap storage
0 likes · 10 min read
Design and Challenges of Tencent iData Analysis Center Backend: Bitmap Storage and MapReduce Architecture
DataFunTalk
DataFunTalk
Apr 24, 2019 · Big Data

Practical Experience with Druid SQL and Security at Meituan: Challenges, Improvements, and Best Practices

This article presents Meituan's real‑world experience with Apache Druid, detailing the platform’s current usage, the usability, security and stability challenges faced, the principles and architecture of Druid SQL, the enhancements made to schema inference, logging, query safety, and the custom security extensions implemented to achieve fine‑grained access control and SSO integration.

DruidOLAPPlatform
0 likes · 14 min read
Practical Experience with Druid SQL and Security at Meituan: Challenges, Improvements, and Best Practices
dbaplus Community
dbaplus Community
Mar 5, 2019 · Databases

How HTAP and DRDS HTAP Enable Real‑Time OLTP/OLAP Integration

This article explains the concepts of OLTP, OLAP and HTAP, describes the DRDS HTAP architecture—including its engine and storage layers, Fireworks Spark‑based engine, optimizer stages, and streaming capabilities—and demonstrates cross‑database MPP queries and streaming joins while outlining suitable use cases and limitations.

DRDSDatabase ArchitectureHTAP
0 likes · 17 min read
How HTAP and DRDS HTAP Enable Real‑Time OLTP/OLAP Integration
DataFunTalk
DataFunTalk
Feb 18, 2019 · Big Data

Hulu’s Big Data Architecture and Sophon OLAP Cache Layer Overview

This article presents an in‑depth overview of Hulu’s big‑data platform, detailing its multi‑layer architecture, the design and functionality of the Sophon OLAP cache layer, and how Impala is employed for high‑performance query processing and integration with cloud‑native engines.

Data ArchitectureHuluImpala
0 likes · 16 min read
Hulu’s Big Data Architecture and Sophon OLAP Cache Layer Overview
Youzan Coder
Youzan Coder
Feb 13, 2019 · Big Data

Druid OLAP Platform Practice at YouZan: Architecture, Features, and Challenges

YouZan adopted MetaMarket’s Druid OLAP platform—featuring millisecond‑level interactive queries, high availability, horizontal scalability, and rich SQL/API query types—by configuring simple ingestion tasks that automatically manage real‑time and batch data, tiered hot/cold storage, and monitoring, while still facing ingestion limits, lack of joins, and occasional latency spikes.

Apache DruidData PlatformDruid
0 likes · 12 min read
Druid OLAP Platform Practice at YouZan: Architecture, Features, and Challenges
JD Tech
JD Tech
Jan 18, 2019 · Big Data

Technical Overview of JD's New Business Intelligence Platform: Offline OLAP, Real‑time Data, and Visualization Solutions

The article details JD's 2018 upgrade of its Business Intelligence platform, describing how unified offline OLAP with ClickHouse, Spark, and Scala, timeliness optimizations, and a React‑based visualization component library together improve data consistency, performance, and user experience for merchants.

ClickHouseData VisualizationOLAP
0 likes · 7 min read
Technical Overview of JD's New Business Intelligence Platform: Offline OLAP, Real‑time Data, and Visualization Solutions
58 Tech
58 Tech
Dec 28, 2018 · Big Data

Kylin OLAP Platform Architecture, Optimizations, and 58.com Case Study

This article introduces Kylin, a HBase‑based multidimensional analysis platform, explains its architecture and various performance optimizations—including multi‑tenant support, dimension dictionary handling, and cube size estimation—while showcasing a real‑world deployment and case study at 58.com.

Cube OptimizationData WarehouseHBase
0 likes · 14 min read
Kylin OLAP Platform Architecture, Optimizations, and 58.com Case Study
Xianyu Technology
Xianyu Technology
Nov 27, 2018 · Big Data

Millisecond-Scale Multi-Dimensional Data Filtering with HybridDB for MySQL

HybridDB for MySQL delivers millisecond‑scale, multi‑dimensional filtering on billions of rows with hundreds of metrics by combining a high‑performance columnar engine, automatic composite indexes, and a fused MPP‑DAG pipeline, turning half‑day push preparation into seconds while supporting full SQL, spatial, and JSON data.

Columnar StorageHybridDBOLAP
0 likes · 8 min read
Millisecond-Scale Multi-Dimensional Data Filtering with HybridDB for MySQL
58 Tech
58 Tech
Nov 26, 2018 · Big Data

Big Data OLAP Applications and Practices: Insights from Xiaomi and 58.com

The article reviews the 2018 58 Group technology salon on big‑data OLAP, summarizing Xiaomi’s one‑stop OLAP architecture, 58.com’s challenges and solutions using Kylin, Druid, and UnionSQL, and the practical implementations and optimizations that illustrate modern OLAP practices.

Data WarehouseDruidKylin
0 likes · 12 min read
Big Data OLAP Applications and Practices: Insights from Xiaomi and 58.com
dbaplus Community
dbaplus Community
Nov 4, 2018 · Databases

How Spark Turns Traditional Databases into Powerful OLAP Engines

This article examines why traditional relational databases like MySQL struggle with analytical workloads, compares ROLAP and MOLAP approaches, explains Spark’s architecture and its advantages for OLAP, and details how Alibaba Cloud’s DRDS HTAP leverages a Spark‑based engine to deliver real‑time distributed query processing.

Data WarehouseDatabasesHTAP
0 likes · 11 min read
How Spark Turns Traditional Databases into Powerful OLAP Engines
Tencent Cloud Developer
Tencent Cloud Developer
Oct 30, 2018 · Big Data

Big Data Technology Trends and Cloud Data Warehouse Architecture Practices

The article reviews recent big-data trends—from Hadoop’s evolution and Spark’s in-memory advances to emerging storage like Ozone—while detailing data-warehouse models, query-optimizer techniques, and cloud-native architectures that integrate diverse data sources, enabling scalable, AI-ready analytics and modern data-lake capabilities.

Big DataCloud Data WarehouseData Lake
0 likes · 30 min read
Big Data Technology Trends and Cloud Data Warehouse Architecture Practices
Beike Product & Technology
Beike Product & Technology
Sep 28, 2018 · Databases

Using ClickHouse for Large‑Scale User Behavior Analysis at Beike Zhaofang

This article details how Beike Zhaofang leveraged the ClickHouse columnar OLAP database for large‑scale user behavior analysis, covering its architecture, key features, performance benchmarks against other engines, data ingestion pipelines, custom UDFs for funnel and retention metrics, deployment setup, and future enhancements.

ClickHouseData EngineeringFunnel Analysis
0 likes · 13 min read
Using ClickHouse for Large‑Scale User Behavior Analysis at Beike Zhaofang
DataFunTalk
DataFunTalk
Sep 9, 2018 · Big Data

Druid Principles and Their Application in Insurance Data Analytics

This article summarizes a presentation by Ping An Insurance data engineers on Druid’s architecture, core concepts, node roles, tuning strategies, and real-world deployment for insurance analytics, illustrating how Druid enables sub‑second, high‑cardinality OLAP queries and supports both real‑time and batch processing.

Data ArchitectureDruidInsurance
0 likes · 11 min read
Druid Principles and Their Application in Insurance Data Analytics
Architecture Digest
Architecture Digest
Jun 22, 2018 · Databases

Distributed Databases for OLAP: MPP, Hadoop Ecosystem, and Like‑Mesa (ClickHouse/Palo) Overview

This article examines the evolution and classification of distributed databases for OLAP workloads, comparing traditional RDBMS, MPP solutions such as Teradata and Greenplum, Hadoop‑based ecosystems, and newer architectures like ClickHouse and Palo, while highlighting their architectural traits, strengths, and limitations.

ClickHouseHadoopMPP
0 likes · 17 min read
Distributed Databases for OLAP: MPP, Hadoop Ecosystem, and Like‑Mesa (ClickHouse/Palo) Overview
Beike Product & Technology
Beike Product & Technology
Jun 1, 2018 · Big Data

Design and Evolution of Lianjia's Big Data Platform: Architecture, Challenges, and Solutions

This article details Lianjia's journey from a Hadoop‑based 0.0 data platform to a sophisticated 2.0 architecture, describing the three‑layer design, OLAP engine choices, transparent compression techniques, operational challenges, and practical recommendations for building and maintaining large‑scale big data systems.

HadoopKylinOLAP
0 likes · 15 min read
Design and Evolution of Lianjia's Big Data Platform: Architecture, Challenges, and Solutions
High Availability Architecture
High Availability Architecture
May 21, 2018 · Big Data

Interview with Baidu’s Chief Big Data Architect Ma Ruyue on OLAP, HTAP, and Emerging Big Data Technologies

In this interview, Baidu’s senior big‑data architect Ma Ruyue discusses his career transition from Hadoop to online databases, the design philosophy behind Baidu’s Palo ROLAP system, the future of HTAP, and his views on the evolving big‑data ecosystem including Spark, AI, and containerization.

Data ArchitectureDistributed ComputingHTAP
0 likes · 11 min read
Interview with Baidu’s Chief Big Data Architect Ma Ruyue on OLAP, HTAP, and Emerging Big Data Technologies
Beike Product & Technology
Beike Product & Technology
Apr 26, 2018 · Big Data

Chain Home's OLAP Platform and Kylin Usage

This article details Chain Home's OLAP platform architecture and Kylin usage, covering the evolution from early ROLAP to MOLAP multi-dimensional engine, Kylin's basic principles, platform structure, application scenarios, usage specifications, capability extensions, and middleware development.

Apache KylinBig DataChain Home
0 likes · 11 min read
Chain Home's OLAP Platform and Kylin Usage
Hulu Beijing
Hulu Beijing
Feb 28, 2018 · Big Data

How Hulu’s Nesto Engine Delivers Near‑Real‑Time OLAP on TB‑Scale Data

This article introduces Hulu's in‑house OLAP engine Nesto, detailing its near‑real‑time data ingestion, nested data model, TB‑level storage using HBase and Parquet, MPP query execution, custom predicate library, and the overall architecture that enables sub‑second ad‑hoc queries for user analytics.

Big DataColumnar StorageHBase
0 likes · 22 min read
How Hulu’s Nesto Engine Delivers Near‑Real‑Time OLAP on TB‑Scale Data
Qunar Tech Salon
Qunar Tech Salon
Dec 21, 2017 · Big Data

Experience and Optimization Strategies for Apache Kylin in Real-Time OLAP

This article shares a data engineer's three‑year experience using Apache Kylin for real‑time OLAP on petabyte‑scale data, describing the business background, challenges of pre‑computation, cube modeling, dimension reduction, and various optimization techniques such as hierarchy, mandatory, and joint dimensions, as well as precise count‑distinct handling.

Apache KylinBig DataCube Optimization
0 likes · 13 min read
Experience and Optimization Strategies for Apache Kylin in Real-Time OLAP
dbaplus Community
dbaplus Community
Dec 12, 2017 · Databases

MariaDB ColumnStore: Future‑Ready OLAP Architecture, Limits & Migration Tips

This article shares practical experience deploying MariaDB ColumnStore for offline OLAP, covering its MPP‑based architecture, user‑experience benefits, high‑availability options, numerous syntax and data‑type limitations, and detailed scripts for schema conversion and incremental data extraction using pt‑archiver.

ColumnstoreData MigrationMariaDB
0 likes · 17 min read
MariaDB ColumnStore: Future‑Ready OLAP Architecture, Limits & Migration Tips
ITPUB
ITPUB
Sep 22, 2017 · Big Data

How Baidu Waimai Scaled Traffic Analysis with Apache Kylin: A Deep Dive

This article presents a detailed case study of Baidu Waimai's traffic analysis platform, outlining the data challenges of high dimensionality and volume, the evaluation of OLAP engines, the adoption of Apache Kylin for pre‑computation, the end‑to‑end data modeling, cube construction, incremental builds, and integration with Saiku‑Mondrian reporting, while sharing practical lessons and performance gains.

Apache KylinBig DataOLAP
0 likes · 29 min read
How Baidu Waimai Scaled Traffic Analysis with Apache Kylin: A Deep Dive
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Aug 25, 2017 · Big Data

How to Optimize OLAP Cubes with Rubik: Dimensional Reduction Strategies Explained

This article walks through Rubik's OLAP cube reduction techniques—including aggregation groups, required, combined, derived, hierarchical, and partial cubes—by designing and implementing buyers and suppliers cubes with six tables, demonstrating performance gains through pre‑computed queries and SQL examples.

CubeData WarehouseDimensional Reduction
0 likes · 10 min read
How to Optimize OLAP Cubes with Rubik: Dimensional Reduction Strategies Explained
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Aug 18, 2017 · Big Data

Cut OLAP Cube Storage Explosions: Proven Dimensionality Reduction Tricks with Rubik

This article explains why raw OLAP Cubes consume exponential storage, then details six practical dimensionality‑reduction methods—Aggregation Group, Mandatory Dimension, Joint Dimension, Derived Dimension, Hierarchy Dimension, and Partial Cube—showing how each can dramatically shrink materialized tables while preserving query performance.

Cube OptimizationData WarehousingOLAP
0 likes · 10 min read
Cut OLAP Cube Storage Explosions: Proven Dimensionality Reduction Tricks with Rubik
dbaplus Community
dbaplus Community
Jul 16, 2017 · Big Data

How Vipshop Scaled Real‑Time OLAP: From GreenPlum to Presto, Kylin, and Redis

Vipshop faced massive data growth that broke traditional RDBMS, causing slow OLAP queries, inefficient ETL, and long development cycles, so it iteratively rebuilt its analytics stack—adding Hadoop/Hive, a self‑service UI, Presto, Kylin, and Redis—to achieve sub‑second query responses, higher concurrency, and a flexible, low‑latency BI solution.

Data WarehouseKylinOLAP
0 likes · 23 min read
How Vipshop Scaled Real‑Time OLAP: From GreenPlum to Presto, Kylin, and Redis
21CTO
21CTO
Jun 14, 2017 · Big Data

How Apache Kylin Supercharges Big Data Analytics with Pre‑Computed Cubes

Apache Kylin is an open‑source, distributed OLAP engine built on Hadoop that uses pre‑computed cubes to deliver sub‑second, high‑concurrency SQL queries on massive datasets, integrates with popular BI tools, offers a modular architecture, recent 1.5.x enhancements, and extensive deployment options.

Apache KylinHadoopOLAP
0 likes · 17 min read
How Apache Kylin Supercharges Big Data Analytics with Pre‑Computed Cubes
Baidu Waimai Technology Team
Baidu Waimai Technology Team
May 16, 2017 · Big Data

Analysis of OLTP/OLAP Integrated Solutions: Apache Phoenix, Apache Trafodion, and Splice Machine

This article examines the convergence of OLTP and OLAP by introducing Apache Phoenix, Apache Trafodion, and Splice Machine, compares their technical features, and describes how Baidu Waimai adopted a Phoenix‑based solution to address scalability and performance challenges in its operational data store.

Apache PhoenixApache TrafodionBig Data
0 likes · 12 min read
Analysis of OLTP/OLAP Integrated Solutions: Apache Phoenix, Apache Trafodion, and Splice Machine
Hulu Beijing
Hulu Beijing
Dec 20, 2016 · Big Data

How Hulu Supercharges OLAP Queries with CarbonData: Real‑World Optimizations

This article describes Hulu’s real‑world OLAP query optimization, covering the fundamentals of OLAP, comparisons of row‑ and column‑based storage formats, detailed indexing mechanisms of Parquet, ORC and CarbonData, and the specific schema, shuffle, block size, speculation and GC tuning techniques that enabled CarbonData to dramatically accelerate wide‑table queries on SparkSQL.

Big DataCarbonDataColumnar Storage
0 likes · 17 min read
How Hulu Supercharges OLAP Queries with CarbonData: Real‑World Optimizations
Ctrip Technology
Ctrip Technology
Sep 2, 2016 · Big Data

Why Druid? Architecture, Indexing, Use Cases, and Lessons Learned

This article introduces Druid as an open‑source, distributed column‑store OLAP engine, explains its architecture and indexing mechanisms, discusses real‑time and batch data ingestion for order analytics at Qunar, compares it with other engines, and shares practical tips and pitfalls.

CaravelDruidOLAP
0 likes · 8 min read
Why Druid? Architecture, Indexing, Use Cases, and Lessons Learned
Ctrip Technology
Ctrip Technology
Aug 26, 2016 · Big Data

Exploring OLAP Engine with Apache Kylin: Architecture, Theory, and Practical Applications in Flight Ticket Big Data

This article presents a comprehensive overview of the Qdata session on OLAP engine exploration, detailing the limitations of traditional MySQL‑based solutions, the requirements for large‑scale analytics, the architecture and theoretical foundations of Apache Kylin, its cube construction process, storage in HBase, query rewriting, real‑world flight‑ticket data applications, and the encountered challenges with corresponding optimization practices.

Apache KylinCubeData Warehouse
0 likes · 7 min read
Exploring OLAP Engine with Apache Kylin: Architecture, Theory, and Practical Applications in Flight Ticket Big Data
Qunar Tech Salon
Qunar Tech Salon
Aug 16, 2016 · Big Data

Exploring OLAP Engine with Apache Kylin: Architecture, Theory, and Applications in Qunar's Big Data Platform

This article presents Qunar's experience transitioning from MySQL‑based OLAP to Apache Kylin, detailing the performance challenges, required features, Kylin's architecture and theory, cube construction process, storage mechanisms, real‑world applications, and the pitfalls and optimization practices discovered along the way.

Apache KylinCubeHBase
0 likes · 6 min read
Exploring OLAP Engine with Apache Kylin: Architecture, Theory, and Applications in Qunar's Big Data Platform
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Jul 14, 2016 · Big Data

What Makes Huawei’s CarbonData a Game-Changer for Big Data Analytics?

Huawei’s CarbonData, now an Apache incubator project, is a lightweight, low‑latency columnar storage format that separates storage and compute, offering multi‑dimensional analytics, high compression, and seamless integration with Spark and Hadoop, while addressing the limitations of traditional NoSQL, search engines, and SQL‑on‑Hadoop solutions.

Apache IncubatorCarbonDataOLAP
0 likes · 14 min read
What Makes Huawei’s CarbonData a Game-Changer for Big Data Analytics?