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416 articles · Page 1 of 5
Yumin Fish Harvest
Yumin Fish Harvest
Aug 13, 2026 · Databases

How to Choose Storage for Query Services: Relational DB, Search, OLAP, and Time‑Series Models

The article walks through a systematic method for selecting the right storage technology—relational OLTP, search engine, OLAP, or time‑series—based on query intent, data volume, latency tolerance, and consistency needs, using an e‑commerce “optimal store” scenario to illustrate the decision process and practical trade‑offs.

CDColapread-model
0 likes · 30 min read
How to Choose Storage for Query Services: Relational DB, Search, OLAP, and Time‑Series Models
Ray's Galactic Tech
Ray's Galactic Tech
Aug 7, 2026 · Databases

OLAP Engine Selection for Billion‑Row Analytics: Doris, ClickHouse, StarRocks

This guide compares Doris, ClickHouse, and StarRocks for billion‑row real‑time analytics, outlining their strengths and weaknesses across write throughput, update handling, join performance, concurrency, cost, and operational complexity, and provides concrete modeling, ingestion, query governance, and fault‑tolerance recommendations to help teams choose the most suitable OLAP platform.

ClickHouseDorisFlink
0 likes · 32 min read
OLAP Engine Selection for Billion‑Row Analytics: Doris, ClickHouse, StarRocks
YiSu Grain
YiSu Grain
Jul 28, 2026 · Big Data

Data Warehouse vs Data Lake vs Lambda/Kappa: Choosing the Right Architecture

This article explains why online transaction databases (OLTP) should not be used for multi‑year analytics, outlines the four core characteristics of a data warehouse, details the ETL process, compares star and snowflake schemas, contrasts data warehouses with data lakes, and guides you through selecting Lambda or Kappa architectures using a concrete retail‑store scenario.

Data LakeData WarehouseETL
0 likes · 36 min read
Data Warehouse vs Data Lake vs Lambda/Kappa: Choosing the Right Architecture
StarRocks
StarRocks
Jul 15, 2026 · Big Data

Building a Lake‑Stream Integrated Data Pipeline with Fluss, Paimon, and StarRocks

The article details how Taotian Group’s senior data engineer Zhu Ao designed a lake‑stream integrated architecture using Fluss for second‑level real‑time storage, Paimon for minute‑level lakehouse persistence, and StarRocks as a unified OLAP query layer, achieving over 50% faster development and more than 80% cost reduction.

Data IntegrationFlussLakehouse
0 likes · 14 min read
Building a Lake‑Stream Integrated Data Pipeline with Fluss, Paimon, and StarRocks
ITPUB
ITPUB
Jul 2, 2026 · Industry Insights

How ColdFront Sets pgEdge Apart in the OLTP‑OLAP‑AI Showdown

The article compares four emerging data‑lake‑for‑PostgreSQL solutions—Databricks LTAP, EDB Fusion Analytics, Snowflake pg_lake, and pgEdge's ColdFront—highlighting ColdFront's unique transparent Iceberg layer, writable cold data, DuckDB integration, and the strategic trade‑offs developers must weigh when choosing a modern OLTP/OLAP/AI architecture.

ColdFrontData LakeDuckDB
0 likes · 9 min read
How ColdFront Sets pgEdge Apart in the OLTP‑OLAP‑AI Showdown
Su San Talks Tech
Su San Talks Tech
May 3, 2026 · Databases

How to Speed Up a 10‑Table JOIN Query in MySQL (JD Interview Scenario)

When a production MySQL query joins ten tables and exceeds 30 seconds, a systematic approach—starting with EXPLAIN analysis, profiling, and configuration checks—combined with layered optimizations such as indexing, join order adjustment, query splitting, temporary tables, materialized views, OLAP engines, and vertical partitioning—can dramatically improve performance.

IndexingJOINMySQL
0 likes · 17 min read
How to Speed Up a 10‑Table JOIN Query in MySQL (JD Interview Scenario)
TonyBai
TonyBai
Mar 13, 2026 · Backend Development

Why DuckDB Beats ClickHouse for Light‑Weight Analytics: 18 M Rows/sec in a Single Go Binary

The article analyzes why traditional row‑oriented databases struggle with high‑volume analytics, introduces DuckDB as an embedded columnar engine for Go, presents benchmark results of up to 18.6 M rows per second writes and 6 M rows per second scans, walks through the Appender API code, and outlines the trade‑offs and ideal hybrid architecture.

DuckDBEmbedded DatabaseGo
0 likes · 11 min read
Why DuckDB Beats ClickHouse for Light‑Weight Analytics: 18 M Rows/sec in a Single Go Binary
Tech Musings
Tech Musings
Feb 12, 2026 · Databases

From MySQL to Apache Doris: Key Design Shifts for OLAP Migration

This article explains how backend engineers should rethink table design, indexing, partitioning, and key strategies when migrating attendance data from MySQL's OLTP model to Apache Doris 2.1.7's OLAP architecture, providing concrete DDL examples and practical tips.

Apache DorisDatabase MigrationIndexing
0 likes · 12 min read
From MySQL to Apache Doris: Key Design Shifts for OLAP Migration
ITPUB
ITPUB
Feb 7, 2026 · Databases

Why MySQL Excels at Transactions but Falters in Analytics – DuckDB to the Rescue

The article examines why MySQL’s transaction layer remains robust while its analytical capabilities lag, outlines enterprise demands for simultaneous OLTP and OLAP workloads, introduces DuckDB’s lightweight yet powerful engine, describes AliSQL’s integration of DuckDB for full MySQL compatibility, and presents TPCH benchmark results showing a dramatic performance boost for analytical queries.

AliSQLDuckDBMySQL
0 likes · 23 min read
Why MySQL Excels at Transactions but Falters in Analytics – DuckDB to the Rescue
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Feb 4, 2026 · Big Data

How Paimon + StarRocks Power Real‑Time OLAP for Double‑11 Mega‑Sales

During Double‑11 mega‑sales, Taobao Group faced exploding OLAP query traffic, costly data sync pipelines, and slow near‑real‑time analytics, so they unified real‑time and batch data in Paimon, leveraged StarRocks for high‑performance lake queries, tuned cluster settings, and saved nearly ten‑million yuan annually while cutting refresh latency by 80%.

Big DataData LakePaimon
0 likes · 22 min read
How Paimon + StarRocks Power Real‑Time OLAP for Double‑11 Mega‑Sales
Data Integration and Governance
Data Integration and Governance
Feb 2, 2026 · Databases

What’s the Difference Between OLTP, OLAP, and HTAP Databases?

OLTP handles real‑time transactional workloads with low latency and high concurrency, OLAP is optimized for large‑scale analytical queries on historical data, and HTAP combines both to support simultaneous transaction processing and analytics; the article compares their characteristics, typical use cases, and popular implementations to guide database selection.

Data AnalyticsDatabase TypesHTAP
0 likes · 9 min read
What’s the Difference Between OLTP, OLAP, and HTAP Databases?
Data Integration and Governance
Data Integration and Governance
Jan 23, 2026 · Big Data

Essential Data Warehouse Terms Every Developer Should Master

This guide explains the core data‑warehouse terminology—from ODS, DWD, DWS, and ADS layers, through fact and dimension tables, SCD handling, partitioning and bucketing, to data quality dimensions, common data issues, ETL vs. ELT, data lake vs. warehouse, and OLTP/OLAP concepts—providing a complete reference for new data engineers.

Data ModelingData WarehouseETL/ELT
0 likes · 10 min read
Essential Data Warehouse Terms Every Developer Should Master
StarRocks
StarRocks
Jan 22, 2026 · Big Data

How Paimon + StarRocks Accelerates Double‑11 OLAP Queries by 80% Refresh Speed

This article explains how Taotian Group unified real‑time and offline data using Paimon as lake storage and StarRocks for high‑performance OLAP, eliminating costly sync pipelines, cutting refresh time by about 80%, saving nearly ten million yuan annually, and detailing the architecture, cluster safeguards, configuration tweaks, monitoring, and future roadmap for large‑scale promotional events.

Big DataPaimonReal-Time Analytics
0 likes · 24 min read
How Paimon + StarRocks Accelerates Double‑11 OLAP Queries by 80% Refresh Speed
Data Integration and Governance
Data Integration and Governance
Jan 16, 2026 · Big Data

Understanding Data Warehouses: Differences from Databases and How to Get Started

The article explains why scattered, unprepared data hampers analysis, defines data warehouses versus traditional databases, outlines the three‑layer architecture (ODS/Staging, DWD, DWS/ADS), details the ETL process, introduces dimensional modeling, and discusses metadata, data‑quality management, and emerging cloud‑native and real‑time trends.

Cloud Data WarehouseData WarehouseETL
0 likes · 9 min read
Understanding Data Warehouses: Differences from Databases and How to Get Started
Big Data Tech Team
Big Data Tech Team
Jan 15, 2026 · Big Data

Mastering Data Warehousing: Core Concepts, Tools, and Future Trends

This article outlines a comprehensive roadmap for data warehousing, covering fundamental concepts, essential big‑data tools, practical implementation steps, advanced architectural topics, and emerging trends such as cloud‑native warehouses and machine‑learning integration, helping readers build a solid knowledge base.

Cloud Data WarehouseData ModelingData Warehouse
0 likes · 9 min read
Mastering Data Warehousing: Core Concepts, Tools, and Future Trends
StarRocks
StarRocks
Oct 28, 2025 · Databases

How Cisco Migrated from Pinot to StarRocks and Boosted Query Performance by Up to 70%

This article details Cisco Webex's migration from a complex Pinot‑Trino OLAP stack to StarRocks, covering the challenges of the legacy system, the step‑by‑step migration process—including storage, compute, and SQL dialect transformation—and the resulting performance gains, cost reductions, and operational improvements.

Big DataMigrationPinot
0 likes · 23 min read
How Cisco Migrated from Pinot to StarRocks and Boosted Query Performance by Up to 70%
Big Data Tech Team
Big Data Tech Team
Oct 12, 2025 · Databases

Why ClickHouse Dominates OLAP: Features, Configurations, Table Engines and Real‑World Use Cases

This article provides an in‑depth technical overview of ClickHouse, covering its OLAP‑focused architecture, key performance features, detailed configuration files, a comprehensive comparison of its many table engines, common troubleshooting tips, and real‑world deployment patterns for recommendation and advertising systems.

ClickHouseKafka EngineMergeTree
0 likes · 68 min read
Why ClickHouse Dominates OLAP: Features, Configurations, Table Engines and Real‑World Use Cases
DataFunTalk
DataFunTalk
Oct 8, 2025 · Big Data

How ByteHouse Cuts Data Warehouse Costs: Tackling Explicit and Implicit Challenges

As data volumes explode, enterprises struggle with the high hardware, performance, operational, and migration costs of traditional OLAP warehouses, but ByteHouse’s cloud‑native architecture offers a cost‑effective, high‑performance solution that dramatically reduces both explicit and hidden expenses.

Big DataByteHouseData Warehouse
0 likes · 6 min read
How ByteHouse Cuts Data Warehouse Costs: Tackling Explicit and Implicit Challenges
DataFunTalk
DataFunTalk
Oct 7, 2025 · Big Data

How ByteHouse Tackles Data Warehouse Cost and Efficiency Challenges

This article examines the exploding data volumes that pressure modern enterprises, outlines the explicit and hidden cost challenges of data warehouses, and presents ByteHouse’s cloud‑native architecture and features as a solution for reducing expenses while boosting analytical performance.

ByteHousecloud-nativecost optimization
0 likes · 6 min read
How ByteHouse Tackles Data Warehouse Cost and Efficiency Challenges
DataFunTalk
DataFunTalk
Sep 29, 2025 · Big Data

How ByteHouse Cuts Data Warehouse Costs While Boosting Performance

This article examines the exploding data volumes that pressure modern enterprises, outlines the explicit (hardware, performance) and implicit (operations, migration) cost challenges of OLAP data warehouses, and presents ByteHouse’s cloud‑native architecture and features as a solution for cost reduction and efficiency gains.

ByteHouseCloud Nativecost optimization
0 likes · 6 min read
How ByteHouse Cuts Data Warehouse Costs While Boosting Performance
DataFunSummit
DataFunSummit
Sep 28, 2025 · Big Data

How ByteHouse Cuts Data Warehouse Costs: Tackling Hidden and Visible Expenses

This article examines the exploding data volumes that pressure modern enterprises, outlines the explicit (hardware, performance) and implicit (operations, migration) costs of operating an OLAP‑based data warehouse, and explains how ByteHouse’s cloud‑native architecture reduces both cost categories while delivering real‑time analytics.

Big DataByteHouseData Warehouse
0 likes · 5 min read
How ByteHouse Cuts Data Warehouse Costs: Tackling Hidden and Visible Expenses
StarRocks
StarRocks
Sep 23, 2025 · Databases

How Zepto Scaled Real‑Time Brand Analytics with StarRocks: From Postgres MVP to Sub‑Second Queries

Zepto transformed its brand‑analytics platform from a Postgres MVP into a production‑grade, sub‑second real‑time analytics solution by adopting StarRocks, redesigning its data pipeline with Databricks, Kafka, and Flink, and choosing a storage‑compute architecture that supports massive joins and rapid insights.

Data PipelineDatabricksFlink
0 likes · 14 min read
How Zepto Scaled Real‑Time Brand Analytics with StarRocks: From Postgres MVP to Sub‑Second Queries
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 11, 2025 · Databases

How StarRocks Unified Data Warehouse Simplified Our Multi-Source Advertising Platform

This article explains how the Tianji advertising platform consolidated heterogeneous MySQL, ClickHouse, and TiDB data sources into a single StarRocks data warehouse, addressing data silos, real‑time performance, and query complexity while improving accuracy, latency, and development efficiency.

Advertising PlatformData WarehouseDatabase Migration
0 likes · 15 min read
How StarRocks Unified Data Warehouse Simplified Our Multi-Source Advertising Platform
JD Cloud Developers
JD Cloud Developers
Sep 2, 2025 · Databases

Unlocking ClickHouse’s Lightning‑Fast Queries: The ‘Nine Swords’ Architecture Explained

This article explores ClickHouse’s high‑performance OLAP design—including its MPP architecture, columnar storage, vectorized execution, pre‑sorting, sharding, replication, index strategies, and compute engine—showing how each innovation contributes to ultra‑fast, scalable data analysis in the big‑data era.

ClickHouseShardingcolumnar storage
0 likes · 14 min read
Unlocking ClickHouse’s Lightning‑Fast Queries: The ‘Nine Swords’ Architecture Explained
Big Data Tech Team
Big Data Tech Team
Aug 24, 2025 · Big Data

Top 18 Data Warehouse Engineer Interview Questions from Meituan and ByteDance

This article compiles 18 essential interview topics for data warehouse engineer roles, covering self‑introduction, architecture layering, dimensional modeling, HDFS operations, Spark vs MapReduce, join implementation, SQL challenges, OLAP selection, real‑time quality assurance, and job transition considerations.

Data WarehouseHDFSSQL
0 likes · 3 min read
Top 18 Data Warehouse Engineer Interview Questions from Meituan and ByteDance
StarRocks
StarRocks
Aug 6, 2025 · Databases

How Qunar Migrated to StarRocks: Architecture, Performance Gains & Best Practices

This article details Qunar's transition to StarRocks as a unified OLAP engine, covering the business background, engine evaluation, architecture redesign, observability, high‑availability strategies, query‑performance optimizations, real‑world application cases, community contributions, and future plans.

High AvailabilityMigrationStarRocks
0 likes · 21 min read
How Qunar Migrated to StarRocks: Architecture, Performance Gains & Best Practices
Lin is Dream
Lin is Dream
May 25, 2025 · Databases

Master MySQL Row-to-Column Pivot: From Basics to Advanced Aggregations

This article walks through using MySQL to pivot rows into columns, covering storage concepts, practical CASE‑WHEN aggregation techniques, and detailed SQL examples for sum, count, average and max calculations, while also discussing performance considerations for medium‑scale and large‑scale data workloads.

AggregationData WarehouseMySQL
0 likes · 7 min read
Master MySQL Row-to-Column Pivot: From Basics to Advanced Aggregations
JD Tech
JD Tech
May 13, 2025 · Databases

Unlock ClickHouse’s Lightning‑Fast Queries: Architecture, Storage, and Index Secrets

This article examines ClickHouse’s high‑performance OLAP design, covering its MPP architecture, columnar storage, vectorized execution, pre‑sorting, table engines, extensive data‑type system, sharding and replication strategies, as well as its sparse and skip‑index mechanisms that together enable ultra‑fast analytics on massive datasets.

Big DataClickHouseData Partitioning
0 likes · 16 min read
Unlock ClickHouse’s Lightning‑Fast Queries: Architecture, Storage, and Index Secrets
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Apr 27, 2025 · Big Data

Scaling Property Services: StarRocks‑Powered Storage‑Compute Separation for 8000+ Communities

Facing a flood of data from over 8,000 communities, the Bifeng service team migrated from a monolithic storage‑compute architecture to a StarRocks‑based storage‑compute separation solution, achieving lower costs, higher resource utilization, faster queries, and improved SLA across their property management platform.

Big DataData WarehouseInfrastructure Migration
0 likes · 11 min read
Scaling Property Services: StarRocks‑Powered Storage‑Compute Separation for 8000+ Communities
StarRocks
StarRocks
Apr 24, 2025 · Databases

Inside StarRocks Optimizer: Architecture, Multi‑Stage Optimization, and Advanced Features

This article provides a comprehensive technical overview of StarRocks' query optimizer, covering its evolution, core architecture, multi‑stage optimization pipeline, key optimizations such as multi‑join colocate, low‑cardinality global dictionary, MV union rewrite, and advanced mechanisms like cost‑estimation fixes, query feedback, adaptive execution, runtime filters, join‑reorder strategies, and SQL plan management.

Adaptive ExecutionCost-Based OptimizationMaterialized Views
0 likes · 26 min read
Inside StarRocks Optimizer: Architecture, Multi‑Stage Optimization, and Advanced Features
Alimama Tech
Alimama Tech
Apr 10, 2025 · Big Data

Performance Optimization of Apache Paimon in Dolphin OLAP Engine

The article details how Apache Paimon, integrated as an external table format in Alibaba’s Dolphin OLAP engine, achieves millisecond‑level query latency and up to 10k QPS through ORC push‑down, manifest conversion, caching, concurrency, and encoding optimizations, outperforming StarRocks and Hologres.

DolphinMetadataOptimization
0 likes · 17 min read
Performance Optimization of Apache Paimon in Dolphin OLAP Engine
JD Retail Technology
JD Retail Technology
Apr 8, 2025 · Databases

ClickHouse Architecture and Core Technologies Overview

ClickHouse is an open‑source, massively parallel, column‑oriented OLAP database that integrates its own columnar storage, vectorized batch processing, pre‑sorted data, diverse table engines, extensive data types, sharding with replication, sparse primary‑key and skip indexes, and a multithreaded query engine, delivering high‑throughput real‑time analytics on massive datasets.

Big DataClickHouseDistributed Architecture
0 likes · 15 min read
ClickHouse Architecture and Core Technologies Overview
StarRocks
StarRocks
Feb 27, 2025 · Big Data

How iQIYI Boosted Ad Query Performance 400% with StarRocks – A Deep Dive into OLAP Evolution

This article details iQIYI's transition from Impala+Kudu and ClickHouse to StarRocks, describing the OLAP architecture, performance gains of up to 400% in advertising workloads, the technical challenges of data consistency, lake‑warehouse fusion, operational scaling, and the step‑by‑step migration process using a dual‑run platform.

ClickHouseFlinkStarRocks
0 likes · 15 min read
How iQIYI Boosted Ad Query Performance 400% with StarRocks – A Deep Dive into OLAP Evolution
Alimama Tech
Alimama Tech
Feb 21, 2025 · Industry Insights

How Paimon + Dolphin Transform Alibaba’s Brand Data Warehouse for Real‑Time Insights

This article analyzes the challenges of Alibaba Mama's brand advertising data warehouse built on a Lambda architecture, introduces Apache Paimon lake storage and Dolphin OLAP engine as a unified lakehouse solution, details implementation steps, performance gains, and business benefits across multiple advertising scenarios.

Big DataData WarehouseDolphin
0 likes · 15 min read
How Paimon + Dolphin Transform Alibaba’s Brand Data Warehouse for Real‑Time Insights
ByteDance Data Platform
ByteDance Data Platform
Jan 9, 2025 · Databases

Why ByteHouse’s GIS Engine Beats Traditional Spatial Databases in Real‑World Analytics

This article explains how ByteHouse integrates high‑performance GIS capabilities into its OLAP engine, describes its spatial indexing architecture, showcases benchmark results against ClickHouse, StarRocks, PostGIS and DuckDB using the NYC Taxi dataset, and outlines when to choose ByteHouse versus other spatial database solutions.

ByteHouseDatabase PerformanceGIS
0 likes · 11 min read
Why ByteHouse’s GIS Engine Beats Traditional Spatial Databases in Real‑World Analytics
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jan 8, 2025 · Databases

ByteHouse GIS: High‑Performance Geospatial Analytics and Benchmark Comparison with ClickHouse, StarRocks, PostGIS, and DuckDB

The article explains ByteHouse's GIS capabilities, describing its R‑Tree and Google S2 spatial index implementation, OGC‑compatible data types and functions, and presents benchmark results that show ByteHouse outperforming ClickHouse, StarRocks, PostGIS, and DuckDB on key geospatial queries.

ByteHouseGISGeospatial
0 likes · 13 min read
ByteHouse GIS: High‑Performance Geospatial Analytics and Benchmark Comparison with ClickHouse, StarRocks, PostGIS, and DuckDB
JD Tech Talk
JD Tech Talk
Dec 26, 2024 · Databases

Using ClickHouse for Efficient Tag Bitmap Storage and Group Computation in a CDP

This article explains how ClickHouse’s columnar storage, bitmap functions, and distributed architecture can be leveraged to store billions of tag bitmaps, combine them efficiently, and support fast group calculations for customer data platforms, while addressing data‑warehouse integration, storage format, and performance challenges.

Bitmapcolumnar storagedata-warehouse
0 likes · 10 min read
Using ClickHouse for Efficient Tag Bitmap Storage and Group Computation in a CDP
StarRocks
StarRocks
Dec 25, 2024 · Databases

Cutting Costs 40% and Halving Query Latency: Our ClickHouse‑to‑StarRocks Migration

Facing high costs and scaling limits with ClickHouse, we migrated a 4000‑core, 500TB OLAP workload to StarRocks, achieving 40% cost reduction, 50% storage savings, and up to 30× query speedups through storage‑compute separation, materialized‑view rewrites, and extensive performance tuning.

ClickHouseMaterialized ViewsStarRocks
0 likes · 18 min read
Cutting Costs 40% and Halving Query Latency: Our ClickHouse‑to‑StarRocks Migration
Architecture Digest
Architecture Digest
Nov 3, 2024 · Backend Development

Using Easy-Query ORM for Strongly Typed OLTP and OLAP Queries in Java

This article introduces Easy-Query, a Java ORM that offers strong‑typed OLTP and OLAP query capabilities, demonstrates how to define entity classes with many‑to‑many and one‑to‑one relationships, and provides multiple code examples for complex queries, DTO generation, and automatic inclusion of related data.

Easy-QueryOLTPORM
0 likes · 11 min read
Using Easy-Query ORM for Strongly Typed OLTP and OLAP Queries in Java
Tencent Cloud Developer
Tencent Cloud Developer
Nov 1, 2024 · Databases

How TDSQL Dominated Global OLAP & OLTP Benchmarks: Inside the Technical Secrets

Tencent Cloud's TDSQL shattered world records in both TPC‑DS (OLAP) and TPC‑C (OLTP) benchmarks, achieving a 7260 M QphDS score at a cost of 37.52 CNY/kQphDS, and the article explains the three self‑developed technologies—MPP execution, parallel execution framework, and columnar‑vectorized engine—that made this performance possible.

Database PerformanceMPPTDSQL
0 likes · 7 min read
How TDSQL Dominated Global OLAP & OLTP Benchmarks: Inside the Technical Secrets
Big Data Technology & Architecture
Big Data Technology & Architecture
Oct 22, 2024 · Big Data

Key Frameworks and Characteristics of Lakehouse Architecture: A Ground‑Level Perspective

This article reviews the emerging lakehouse architecture, outlines its core frameworks such as Hudi, Iceberg, Paimon, Flink, and Doris, discusses their storage‑compute separation, read‑write optimizations, and highlights how companies of different sizes adopt these technologies based on cost, efficiency, and specific business scenarios.

DorisFlinkLakehouse
0 likes · 6 min read
Key Frameworks and Characteristics of Lakehouse Architecture: A Ground‑Level Perspective
ByteDance Data Platform
ByteDance Data Platform
Oct 16, 2024 · Databases

How ByteHouse Boosted Sales Data Platform Queries Up to 16× with ACL and Optimizer

This article examines a fast‑growing company's sales data platform, outlines the data‑access pain points caused by ACL permissions, describes the migration from ClickHouse to ByteHouse, details the optimizer’s rule‑based, cost‑based, and distributed‑plan enhancements, and presents benchmark results showing query speedups of up to sixteen times.

ACLByteHouseolap
0 likes · 16 min read
How ByteHouse Boosted Sales Data Platform Queries Up to 16× with ACL and Optimizer
Big Data Technology & Architecture
Big Data Technology & Architecture
Oct 16, 2024 · Databases

Kuaishou's Lakehouse‑Integrated OLAP Architecture with Apache Doris: Design, Migration, and Optimization

The article describes how Kuaishou transformed its high‑traffic OLAP system from a separated lake‑and‑warehouse architecture using Hive/Hudi and ClickHouse into a unified lakehouse solution powered by Apache Doris, detailing the challenges, design choices, caching and automatic materialization mechanisms, and the resulting performance and governance improvements.

Apache DorisBig DataData Caching
0 likes · 18 min read
Kuaishou's Lakehouse‑Integrated OLAP Architecture with Apache Doris: Design, Migration, and Optimization
Big Data Technology & Architecture
Big Data Technology & Architecture
Sep 18, 2024 · Databases

Doris Performance Optimization: OLAP Query, Indexes, Vectorized Execution, and High‑Concurrency Point Queries

This article explains how Apache Doris achieves high‑concurrency OLAP and point‑query performance through MPP architecture, columnar storage, partition‑bucket pruning, various indexes, materialized views, vectorized execution, runtime filters, short‑circuit planning, and prepared‑statement caching.

DorisQuery Optimizationhigh concurrency
0 likes · 12 min read
Doris Performance Optimization: OLAP Query, Indexes, Vectorized Execution, and High‑Concurrency Point Queries
DataFunSummit
DataFunSummit
Aug 26, 2024 · Big Data

Building a Doris‑Based Lakehouse Integrated Analytics System at Kuaishou

This article presents Kuaishou's experience of designing and implementing a Doris‑driven lakehouse integrated analytics system, covering the current OLAP landscape, challenges of data duplication and governance, the new architecture with caching and auto‑materialization, implementation details, performance impact, and future work.

Auto MaterializationBig DataData Warehouse
0 likes · 24 min read
Building a Doris‑Based Lakehouse Integrated Analytics System at Kuaishou
Bilibili Tech
Bilibili Tech
Aug 23, 2024 · Big Data

Accelerating Multi‑Dimensional OLAP Queries in ClickHouse with Grouping Sets, RBM, and Dense Dictionary Encoding

To achieve sub‑second, multi‑dimensional analytics on Bilibili’s hundred‑million‑row datasets, the team built a ClickHouse‑based acceleration layer that combines grouping‑set pre‑aggregation, bitmap (RBM) distinct handling, and a dense dictionary encoding service, dramatically cutting CPU, memory and query latency versus traditional OLAP pipelines.

Big DataBitmapClickHouse
0 likes · 28 min read
Accelerating Multi‑Dimensional OLAP Queries in ClickHouse with Grouping Sets, RBM, and Dense Dictionary Encoding
DataFunTalk
DataFunTalk
Aug 6, 2024 · Fundamentals

Solving Massive Data Retrieval Demands: From Problem Causes to OLAP Multidimensional Reporting Solutions

This article analyzes why data engineers face endless data‑extraction requests, identifies common missteps in data‑construction practices, and proposes a comprehensive solution based on dimensional modeling, OLAP multidimensional reporting, self‑service tools, and knowledge empowerment to dramatically improve efficiency and scalability.

Data Engineeringdimensional modelingolap
0 likes · 12 min read
Solving Massive Data Retrieval Demands: From Problem Causes to OLAP Multidimensional Reporting Solutions
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jul 22, 2024 · Databases

Why StarRocks Is Redefining Fast Unified OLAP Analytics

StarRocks combines vectorized execution, a new cost‑based optimizer, materialized views, a real‑time storage engine, pipeline execution, and distributed joins to deliver a unified, high‑performance OLAP solution that supports both traditional and lakehouse analytics while reducing operational complexity.

CBODatabaseLakehouse
0 likes · 14 min read
Why StarRocks Is Redefining Fast Unified OLAP Analytics
dbaplus Community
dbaplus Community
Jul 10, 2024 · Databases

Why ClickHouse Dominates OLAP Performance: An In‑Depth Architecture Guide

This article explains ClickHouse’s columnar, MPP‑based design, block compression, LSM pre‑sorting, sparse and skip‑list indexing, and vectorized execution, while also discussing its high‑frequency write challenges, concurrency limits, and production‑grade issues such as Zookeeper load and resource management.

ClickHouseColumnar DatabaseIndexing
0 likes · 11 min read
Why ClickHouse Dominates OLAP Performance: An In‑Depth Architecture Guide
DataFunTalk
DataFunTalk
Jul 10, 2024 · Big Data

Apache SeaTunnel: A Next‑Generation Data Integration Platform for ETL/ELT and OLAP

This article introduces Apache SeaTunnel, a modern data integration platform designed for the EtLT era, detailing its architecture, core connector APIs, checkpoint mechanism, model inference, multi‑table synchronization, the high‑performance SeaTunnel Zeta engine, OLAP use cases, community roadmap, and the commercial WhaleTunnel product.

Apache SeaTunnelBig DataELT
0 likes · 22 min read
Apache SeaTunnel: A Next‑Generation Data Integration Platform for ETL/ELT and OLAP
Data Thinking Notes
Data Thinking Notes
May 30, 2024 · Databases

Why Your Data Team Is Drowning in Requests—and How OLAP Can Save You

This article examines why data departments get overwhelmed by massive data‑retrieval requests, identifies root causes such as mindset, requirement handling, and lack of tools, and presents a technical solution centered on dimensional modeling and OLAP multi‑dimensional reporting to streamline data access and empower teams.

Big DataData EngineeringData Modeling
0 likes · 12 min read
Why Your Data Team Is Drowning in Requests—and How OLAP Can Save You
Past Memory Big Data
Past Memory Big Data
Apr 23, 2024 · Big Data

ByConity Replaces ClickHouse for OLAP, Cutting Resource Costs Over 50%

MetaApp replaced ClickHouse with the open‑source cloud‑native warehouse ByConity, achieving more than 50% reduction in resource costs while delivering comparable or faster OLAP query performance across distinct, retention, conversion, and point‑lookup workloads, thanks to compute‑storage separation, read/write isolation, and minute‑level elastic scaling.

ByConityClickHouseCloud Native
0 likes · 15 min read
ByConity Replaces ClickHouse for OLAP, Cutting Resource Costs Over 50%
Past Memory Big Data
Past Memory Big Data
Apr 19, 2024 · Databases

How ByteHouse Achieves Hundred‑Fold OLAP Performance Gains

ByteHouse, a cloud‑native data warehouse built on ClickHouse, redesigns storage‑compute separation, introduces a new MPP architecture, rule‑based and cost‑based optimizers, exchange runtime filters, and parallelism techniques, delivering 10‑200× faster query performance on TPC‑DS, TPC‑H and SSB benchmarks and boosting point‑lookup QPS to 32,000.

ByteHouseData WarehouseQuery Engine
0 likes · 19 min read
How ByteHouse Achieves Hundred‑Fold OLAP Performance Gains
DataFunTalk
DataFunTalk
Apr 15, 2024 · Databases

ByteHouse Cloud‑Native Data Warehouse Performance Whitepaper: Architecture, Optimizations, and Benchmark Results

The ByteHouse performance whitepaper details the cloud‑native data warehouse’s architecture, rule‑based and cost‑based optimizer enhancements, exchange runtime, runtime filters, parallelism and wide‑table optimizations, and presents benchmark comparisons on TPC‑DS, TPC‑H and SSB datasets demonstrating orders‑of‑magnitude query speed improvements.

ByteHouseCloud NativeData Warehouse
0 likes · 17 min read
ByteHouse Cloud‑Native Data Warehouse Performance Whitepaper: Architecture, Optimizations, and Benchmark Results
21CTO
21CTO
Apr 11, 2024 · Databases

Why PostgreSQL Is Becoming the Dominant Database Framework

This article traces PostgreSQL’s evolution from an academic project to a versatile data‑management framework, highlights its extensive extensions, performance benchmarks, and the shifting landscape of OLTP/OLAP, showing how its open‑source extensibility is reshaping the entire database world.

DatabaseOpen SourcePostgreSQL
0 likes · 18 min read
Why PostgreSQL Is Becoming the Dominant Database Framework
StarRocks
StarRocks
Mar 26, 2024 · Big Data

How Replacing Spark with StarRocks Cut Data Refresh Time by 90% and Saved 99% Cost

The article details how the Xiaohongshu data warehouse team integrated StarRocks into their offline processing pipeline, replacing Spark for heavy Cube calculations, which reduced job execution from hours to minutes, cut resource consumption by over 90%, advanced daily data output by 1.5 hours, and lowered refresh cost by more than 99%.

Big DataSparkStarRocks
0 likes · 18 min read
How Replacing Spark with StarRocks Cut Data Refresh Time by 90% and Saved 99% Cost
DataFunSummit
DataFunSummit
Mar 12, 2024 · Big Data

Solving Massive Data Retrieval Demands: From Root Causes to OLAP Multidimensional Reporting Solutions

This article analyzes why data engineers face endless data‑retrieval requests, identifies common missteps in data‑construction such as demand‑driven development, lack of modeling and OLAP concepts, and proposes a dimension‑model‑based data warehouse with OLAP reporting, tooling, and knowledge‑empowerment to break the cycle.

Data Engineeringdimension modelingolap
0 likes · 13 min read
Solving Massive Data Retrieval Demands: From Root Causes to OLAP Multidimensional Reporting Solutions
ByteDance Data Platform
ByteDance Data Platform
Mar 6, 2024 · Databases

How ByteHouse Boosted Douyin’s Interest Circle Queries by 100×

This article explains how Douyin rebuilt its interest‑circle platform by replacing MySQL with the columnar OLAP engine ByteHouse, achieving roughly a hundred‑fold improvement in query speed, lower hardware costs, and seamless horizontal scalability for massive daily data volumes.

ByteHouseColumnarStorageDataWarehouse
0 likes · 10 min read
How ByteHouse Boosted Douyin’s Interest Circle Queries by 100×
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 1, 2024 · Big Data

Scaling U‑App Analytics to Billions of Events with Flink, MaxCompute & Hologres

UMeng+’s U‑App analytics platform processes nearly a trillion daily logs by combining real‑time Flink streams, offline MaxCompute batches, and Alibaba Cloud Hologres OLAP, employing multi‑engine architecture, smart sampling, and Roaring Bitmap techniques to deliver fast, cost‑effective, high‑concurrency user behavior and profiling analysis.

FlinkHologresMaxCompute
0 likes · 19 min read
Scaling U‑App Analytics to Billions of Events with Flink, MaxCompute & Hologres
DataFunSummit
DataFunSummit
Feb 29, 2024 · Big Data

Trino at Xiaomi: Architecture, Practices, and Future Plans

This article details Xiaomi’s practical deployment of Trino, covering its architectural role, core and extended capabilities, performance comparisons, integration with Iceberg and Spark, operational enhancements, multi‑cluster and ad‑hoc query scenarios, future cloud‑storage plans, and a Q&A session.

Big DataIcebergSQL Compatibility
0 likes · 20 min read
Trino at Xiaomi: Architecture, Practices, and Future Plans
Volcano Engine Developer Services
Volcano Engine Developer Services
Feb 29, 2024 · Big Data

How MetaApp Cut Data Warehouse Costs by 50% with ByConity

MetaApp replaced ClickHouse with the open‑source cloud‑native data warehouse ByConity, achieving over 50% cost reduction and faster, more stable OLAP queries by separating storage and compute, simplifying scaling, and improving resource utilization across a range of analytics workloads such as deduplication, retention, conversion and point‑lookup.

ByConityClickHouseData Warehouse
0 likes · 13 min read
How MetaApp Cut Data Warehouse Costs by 50% with ByConity
Didi Tech
Didi Tech
Feb 27, 2024 · Big Data

Real-time Precise Deduplication Using StarRocks Materialized Views at Didi

Didi leverages StarRocks materialized views with a global dictionary and bitmap aggregation to perform real‑time, high‑cardinality precise deduplication, automatically rewriting queries and refreshing views, cutting query latency by ~80%, reducing resource use ~95%, and boosting concurrent QPS up to 100‑fold, while planning further automation and bitmap optimizations.

Big DataMaterialized ViewsPrecise Deduplication
0 likes · 19 min read
Real-time Precise Deduplication Using StarRocks Materialized Views at Didi
StarRocks
StarRocks
Feb 27, 2024 · Databases

How StarRocks Materialized Views Enable High‑Concurrency Precise Deduplication

StarRocks’ materialized view feature lets Didi replace costly fuzzy deduplication with precise, high‑concurrency deduplication for real‑time dashboards, using global dictionary mapping, layered ODS/DWD/ADS views, synchronous and asynchronous refreshes, and transparent query rewrite to cut query latency by 80% and boost QPS dramatically.

Big DataMaterialized ViewsPrecise Deduplication
0 likes · 20 min read
How StarRocks Materialized Views Enable High‑Concurrency Precise Deduplication
DataFunTalk
DataFunTalk
Feb 27, 2024 · Big Data

Best Practices of Cloud‑Native OLAP Architecture and Logistics Warning at Jushuitan

This article presents Jushuitan's cloud‑native OLAP architecture, detailing its evolution, current big‑data stack—including DataWorks, MaxCompute, Flink, Hologres, and Aerospike—along with logistics warning workflows, rule‑matching mechanisms, real‑time processing challenges, and future scalability plans.

Big DataCloud NativeData Warehouse
0 likes · 20 min read
Best Practices of Cloud‑Native OLAP Architecture and Logistics Warning at Jushuitan
DataFunSummit
DataFunSummit
Feb 7, 2024 · Big Data

Evolution of OLAP with Apache Doris at Xingyun Retail Credit

Facing rapid data growth, Xingyun Retail Credit transitioned from traditional OLTP systems to an Apache Doris‑based OLAP solution, detailing the data demand generation, OLAP engine selection challenges, multi‑stage implementation, performance gains, data‑warehouse construction, and future roadmap for scalable analytics.

Apache DorisBig DataData Warehouse
0 likes · 17 min read
Evolution of OLAP with Apache Doris at Xingyun Retail Credit
Sohu Tech Products
Sohu Tech Products
Jan 31, 2024 · Industry Insights

How Didi Scaled Real‑Time Dashboards with StarRocks Materialized Views

This article details Didi's evolution from a multi‑engine OLAP stack to a unified StarRocks solution, explains the design of global dictionaries and materialized views for real‑time dashboard acceleration, and shares performance results, challenges, and future optimization directions.

Big DataDidiMaterialized Views
0 likes · 19 min read
How Didi Scaled Real‑Time Dashboards with StarRocks Materialized Views
Big Data Technology & Architecture
Big Data Technology & Architecture
Jan 29, 2024 · Databases

Practical Experience of StarRocks Materialized Views at Didi

This article details Didi's evolution of OLAP systems, the adoption of StarRocks for high‑performance MPP analytics, and how materialized views, global dictionary mapping, and transparent acceleration were engineered to boost real‑time dashboard queries while outlining performance gains, challenges, and future optimization plans.

Big DataDidiReal-time Dashboard
0 likes · 16 min read
Practical Experience of StarRocks Materialized Views at Didi
DataFunTalk
DataFunTalk
Jan 28, 2024 · Databases

Practical Experience of StarRocks Materialized Views at Didi

This article presents Didi's practical experience with StarRocks materialized views, covering the evolution of its OLAP architecture, the challenges of previous engines, the adoption of StarRocks, the design of materialized view acceleration for real‑time dashboards, and future optimization directions.

Big DataStarRocksdata platform
0 likes · 17 min read
Practical Experience of StarRocks Materialized Views at Didi
DataFunSummit
DataFunSummit
Jan 25, 2024 · Big Data

Best Practices of Jushuitan Cloud‑Native OLAP Architecture and Logistics Warning

This article presents Jushuitan's cloud‑native OLAP architecture, covering business background, data‑warehouse evolution, real‑time processing with Flink, Hologres, and Aerospike, and detailed logistics‑warning use cases, followed by technical challenges, future outlook, and a Q&A on implementation details.

Big DataData WarehouseFlink
0 likes · 20 min read
Best Practices of Jushuitan Cloud‑Native OLAP Architecture and Logistics Warning
JD Tech
JD Tech
Jan 18, 2024 · Databases

Understanding ClickHouse: Architecture, Principles, and Performance

This article introduces ClickHouse, an open‑source columnar OLAP database, explains its architecture—including columnar storage, block processing, LSM, indexing and vectorized execution—highlights its performance advantages over other engines, and discusses its limitations such as write‑amplification, concurrency constraints, and ZooKeeper dependency.

Big DataClickHouseColumnar Database
0 likes · 12 min read
Understanding ClickHouse: Architecture, Principles, and Performance
dbaplus Community
dbaplus Community
Jan 16, 2024 · Databases

Mastering MariaDB ColumnStore: Installation, Migration, Replication, and Performance Insights

This guide explains MariaDB ColumnStore’s columnar architecture, key advantages, step‑by‑step installation, field and SQL limitations, how to migrate InnoDB data, configure multi‑source replication, compare performance with InnoDB, and achieve high availability and dynamic scaling for analytical workloads.

ColumnstoreData MigrationInstallation
0 likes · 17 min read
Mastering MariaDB ColumnStore: Installation, Migration, Replication, and Performance Insights
DataFunTalk
DataFunTalk
Jan 3, 2024 · Databases

ClickHouse 2024 Core New Features and Product Development Directions

This article introduces ClickHouse, an open‑source columnar OLAP database, outlines its architecture, advantages, self‑hosted and cloud deployment models, highlights recent product features such as async inserts, JSON support, Parquet acceleration, query caching, and summarizes a Q&A covering semi‑structured data, MPP, virtual columns, and future roadmap.

ClickHouseColumnar DatabaseData Warehouse
0 likes · 12 min read
ClickHouse 2024 Core New Features and Product Development Directions
Zhuanzhuan Tech
Zhuanzhuan Tech
Dec 27, 2023 · Big Data

Implementing Self-Service OLAP Analytics with Quick BI and StarRocks: Architecture, Optimizations, and Lessons Learned

This article presents a comprehensive case study of building a self‑service OLAP analytics platform at ZhaiZhai using Quick BI and StarRocks, covering background motivations, technical architecture, implementation details, performance‑optimizing case studies, and the resulting business impact.

Data WarehouseQuick BISelf‑service analytics
0 likes · 16 min read
Implementing Self-Service OLAP Analytics with Quick BI and StarRocks: Architecture, Optimizations, and Lessons Learned
Zhuanzhuan Tech
Zhuanzhuan Tech
Dec 20, 2023 · Big Data

Design and Implementation of Zhaozhuan One-Service Unified Data Query Platform

This article describes the evolution of Zhaozhuan's data services, the design and architecture of the One-Service unified query platform supporting multiple storage engines, its security and intelligent query features, and future plans for finer-grained permission control, multi‑engine support, online service isolation, and improved usability.

Big DataUnified Queryarchitecture
0 likes · 15 min read
Design and Implementation of Zhaozhuan One-Service Unified Data Query Platform
DataFunSummit
DataFunSummit
Dec 16, 2023 · Databases

Optimizing Precise Deduplication with Doris Bitmap: Architecture, Performance Enhancements, and Practical Practices

This article presents a comprehensive overview of precise deduplication in Meituan's Doris database, detailing the underlying bitmap data structures, aggregation bottlenecks, and a series of optimizations—including memory management, fast union, orthogonal encoding, and vectorized engine integration—that together achieve significant performance gains in high‑cardinality scenarios.

BitmapDatabaseDoris
0 likes · 20 min read
Optimizing Precise Deduplication with Doris Bitmap: Architecture, Performance Enhancements, and Practical Practices
DataFunTalk
DataFunTalk
Dec 11, 2023 · Databases

Interview with Wu Li on Columnar Storage, JIT Compilation, and Push Mode in Modern Database Systems

The interview with Wu Li, a research engineer at Shanghai Yanhuang Data, explores how columnar storage, JIT compilation, and push-mode processing are reshaping modern database performance, highlighting hardware constraints, software optimizations, and product‑centric goals in the era of big data analytics.

DatabasesJIT Compilationcolumnar storage
0 likes · 11 min read
Interview with Wu Li on Columnar Storage, JIT Compilation, and Push Mode in Modern Database Systems
DataFunTalk
DataFunTalk
Dec 8, 2023 · Databases

Interview with Wu Li on Database Evolution: Columnar Storage, JIT Compilation, and Push Mode

The article presents an interview with Wu Li, a research engineer at Shanghai Yanhuang Data, discussing how hardware limits have driven database evolution toward columnar storage, the adoption of Apache Arrow and Gandiva for SIMD‑enabled JIT compilation, and the shift from pull to push processing modes to improve OLAP performance.

Apache ArrowGandivadatabase optimization
0 likes · 10 min read
Interview with Wu Li on Database Evolution: Columnar Storage, JIT Compilation, and Push Mode
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Dec 8, 2023 · Cloud Computing

How Alibaba Cloud EMR Powers Serverless StarRocks for Seamless Lakehouse Analytics

This article summarizes Li Yu's presentation on Alibaba Cloud EMR's deep collaboration with the StarRocks community, detailing major contributions across versions, the serverless StarRocks product’s core capabilities, and future plans to enhance OLAP‑lakehouse integration, performance, and cloud‑native elasticity.

Alibaba CloudCloud ComputingEMR
0 likes · 7 min read
How Alibaba Cloud EMR Powers Serverless StarRocks for Seamless Lakehouse Analytics
Baidu Geek Talk
Baidu Geek Talk
Nov 29, 2023 · Databases

How Baidu Built an HTAP Table Storage System to Tackle Massive Data Analytics

This article examines Baidu Search's content storage team's HTAP table storage system, detailing the challenges of supporting massive OLAP workloads on an OLTP‑oriented backend, the architectural split into Neptune and Saturn, storage‑engine optimizations such as row partitioning and dynamic columns, and a SQL‑like KQL framework for compute and scheduling.

DatabaseHTAPKQL
0 likes · 13 min read
How Baidu Built an HTAP Table Storage System to Tackle Massive Data Analytics
DataFunTalk
DataFunTalk
Nov 11, 2023 · Big Data

Streaming Graph Processing in Ant Group: Real-Time Data Architecture and Applications

This article presents Ant Group's comprehensive real-time data framework and streaming graph processing engine, detailing its architecture, unified batch‑stream capabilities, and practical applications such as traffic attribution, real‑time OLAP, and user‑behavior intent analysis, while outlining future directions.

Big DataGraph ProcessingReal‑time Data
0 likes · 15 min read
Streaming Graph Processing in Ant Group: Real-Time Data Architecture and Applications
StarRocks
StarRocks
Oct 31, 2023 · Databases

How Ctrip Accelerated Report Queries 10× with StarRocks: A Real‑World Lakehouse Migration

Ctrip migrated its Artnova reporting platform from Hive‑based queries to StarRocks, first loading data into OLAP tables and then using StarRocks as a lakehouse with Hive catalog, Data Cache and materialized views, achieving average query latency reductions from 20 seconds to 1.5 seconds, over 7× speed‑up versus Trino and up to 40× acceleration for complex workloads.

Big DataData CacheLakehouse
0 likes · 15 min read
How Ctrip Accelerated Report Queries 10× with StarRocks: A Real‑World Lakehouse Migration