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Data Modeling

324 articles · Page 3 of 4
DaTaobao Tech
DaTaobao Tech
May 13, 2022 · Big Data

Taobao Big Data Model Governance and DataWorks Co‑development

Taobao’s rapidly expanding technical data system faced naming inconsistencies, low table reuse, and costly, inefficient data usage, prompting a joint effort with DataWorks to digitize model evaluation, enforce standardized governance, deliver intelligent end‑to‑end modeling tools, and launch a development assistant, resulting in a health‑monitoring dashboard, upgraded data maps, and a roadmap for further automation and architecture refinement.

Data ModelingDataWorksbig data
0 likes · 12 min read
Taobao Big Data Model Governance and DataWorks Co‑development
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 7, 2022 · Big Data

How Alibaba’s Big Data Model Governance Boosted Efficiency and Cut Costs

This article details Alibaba's large‑scale data model governance initiative, analyzing current data issues, presenting a comprehensive solution—including model digitization, public model sinking, productization, daily governance, and search‑enhancement—and outlining achieved results and future plans to further improve data quality, reuse, and operational efficiency.

Data ModelingDataWorksModel Scoring
0 likes · 12 min read
How Alibaba’s Big Data Model Governance Boosted Efficiency and Cut Costs
Taobao Frontend Technology
Taobao Frontend Technology
Mar 17, 2022 · Backend Development

How No‑Code Platforms Can Revolutionize Mid‑Office Page Production and Boost Development Efficiency

This article explains how a scene‑driven, no‑code platform standardizes UI, data, and API models to enable scalable, high‑efficiency production of middle‑office pages, reduces manual coding, improves collaboration across roles, and provides a unified efficiency‑measurement framework for source, low‑code, and no‑code development modes.

Data ModelingNo-codeStandardization
0 likes · 16 min read
How No‑Code Platforms Can Revolutionize Mid‑Office Page Production and Boost Development Efficiency
Python Programming Learning Circle
Python Programming Learning Circle
Feb 25, 2022 · Databases

Rewriting Dagoba: Building an In‑Memory Graph Database in Python

This article walks through the step‑by‑step rewrite of the Dagoba in‑memory graph database from JavaScript to Python, covering data modeling, primary‑key management, eager and lazy query implementations, bidirectional edge support, performance optimizations, and how to extend the query language with custom methods.

Data ModelingIn-Memoryeager query
0 likes · 25 min read
Rewriting Dagoba: Building an In‑Memory Graph Database in Python
Ops Development Stories
Ops Development Stories
Feb 24, 2022 · Big Data

Master Elasticsearch: Core Concepts, APIs, Mapping, and Performance Tuning

This comprehensive guide explains Elasticsearch fundamentals—including documents, indices, nodes, clusters, REST and Document APIs, query DSL, relevance scoring, distributed architecture, real‑time indexing, search execution, pagination, scroll, aggregations, data modeling, mapping options, parent/child relationships, reindexing, and practical cluster and write/read performance optimizations.

AggregationData ModelingElasticsearch
0 likes · 58 min read
Master Elasticsearch: Core Concepts, APIs, Mapping, and Performance Tuning
DataFunTalk
DataFunTalk
Feb 3, 2022 · Big Data

Improving Data Processing Efficiency at Kuaishou with Apache Hudi

This article explains how Kuashou tackled latency and efficiency problems in large‑scale data pipelines by adopting Apache Hudi, detailing the pain points, reasons for choosing Hudi, its architecture, model design, handling of bursty updates, back‑fill scenarios, and operational safeguards.

Data LakeData ModelingFlink
0 likes · 13 min read
Improving Data Processing Efficiency at Kuaishou with Apache Hudi
JD Tech
JD Tech
Dec 24, 2021 · Databases

A Methodology for Translating Business Models into Data Models: Community Group Buying Case Study

This article presents a step‑by‑step methodology for converting business domain models into robust data models, illustrated with a community group‑buying pre‑scheduling case, and discusses object identification, relationship definition, model refinement, and mapping to database structures to improve scalability and maintainability.

Business AnalysisData ModelingSoftware Engineering
0 likes · 9 min read
A Methodology for Translating Business Models into Data Models: Community Group Buying Case Study
Big Data Technology & Architecture
Big Data Technology & Architecture
Nov 28, 2021 · Big Data

OneData Methodology: Building a Unified Data Warehouse Architecture and Governance Framework

This article presents the OneData methodology for designing, standardizing, and governing a data warehouse, detailing background challenges, goals, industry references, core concepts, unified business and design consolidation, data modeling layers, naming conventions, data quality controls, and the resulting operational improvements and business value.

Data ModelingOneDatabig data
0 likes · 20 min read
OneData Methodology: Building a Unified Data Warehouse Architecture and Governance Framework
Hacker Afternoon Tea
Hacker Afternoon Tea
Nov 26, 2021 · Big Data

How to Write and Test Snuba Queries in Sentry’s Data Platform

This guide walks through discovering Snuba datasets and entities, inspecting their schemas, building queries with the Python SDK or Sentry code, testing via the Web UI or curl, and interpreting request, response, and debugging information including HTTP status codes and ClickHouse metrics.

ClickHouseData ModelingPython SDK
0 likes · 10 min read
How to Write and Test Snuba Queries in Sentry’s Data Platform
Ctrip Technology
Ctrip Technology
Nov 5, 2021 · Backend Development

Design Principles and Architecture Evolution of Ctrip Business Travel Order System

The article details Ctrip Business Travel's order system architecture, outlining its background, challenges of the original vertical‑only design, and the layered, process‑driven, and snapshot design principles that guide its evolution toward modular backend services, data consolidation, configurable messaging, and service orchestration.

Data ModelingService OrchestrationTravel Technology
0 likes · 17 min read
Design Principles and Architecture Evolution of Ctrip Business Travel Order System
Architect's Tech Stack
Architect's Tech Stack
Oct 20, 2021 · Databases

MySQL Performance Optimization: Data Volume, Concurrency, Query Time, Table Design, Index and SQL Tuning

This article presents a comprehensive guide to MySQL performance, covering maximum data volume and concurrency limits, recommended query response times, practical table‑design rules, index classification and optimization techniques, as well as detailed SQL tuning patterns such as batch processing, operator, OR, IN, LIKE, JOIN and LIMIT improvements.

Data ModelingDatabase OptimizationIndexing
0 likes · 15 min read
MySQL Performance Optimization: Data Volume, Concurrency, Query Time, Table Design, Index and SQL Tuning
21CTO
21CTO
Oct 13, 2021 · Backend Development

When to Adopt CQRS? Balancing Read/Write Models for Scalable Backend Systems

This article examines the challenges of evolving data models in large applications, explains the Command Query Responsibility Segregation (CQRS) pattern, and outlines when to adopt or avoid it to balance read/write performance, scalability, and system complexity.

CQRSData Modelingbackend architecture
0 likes · 11 min read
When to Adopt CQRS? Balancing Read/Write Models for Scalable Backend Systems
Hacker Afternoon Tea
Hacker Afternoon Tea
Oct 9, 2021 · Databases

Understanding Snuba’s Data Model: Logical vs Physical Architecture

The article explains Snuba’s data organization by separating a logical model visible to clients from a physical model that maps to ClickHouse tables, detailing datasets, entity types, relationships, consistency guarantees, storage layers, and concrete examples of single and multi‑entity datasets.

ClickHouseData ModelingSentry
0 likes · 10 min read
Understanding Snuba’s Data Model: Logical vs Physical Architecture
Architect
Architect
Sep 16, 2021 · Fundamentals

Object Modeling: Comparing Object and Data Models, OOP Principles, and Composition vs Aggregation

This article explains the philosophical basis of object‑oriented thinking, distinguishes objects from things, discusses attributes and methods, compares object‑oriented and data‑model designs with Java and SQL examples, and clarifies composition and aggregation through real‑world and code illustrations.

AggregationCompositionData Modeling
0 likes · 13 min read
Object Modeling: Comparing Object and Data Models, OOP Principles, and Composition vs Aggregation
IT Architects Alliance
IT Architects Alliance
Sep 12, 2021 · Industry Insights

Data Warehouse vs. Database: Core Differences and Building a Data Platform

This article explains what a data warehouse is, contrasts it with traditional databases, outlines how to design and build a data warehouse—including model selection, topic domain division, bus matrix, layered architecture, and data governance—then expands to the concept of a data middle platform and its distinction from data lakes and big‑data platforms.

Data Modelingbig datadata governance
0 likes · 18 min read
Data Warehouse vs. Database: Core Differences and Building a Data Platform
Architects' Tech Alliance
Architects' Tech Alliance
Sep 11, 2021 · Big Data

Understanding Data Warehouses: Definitions, Differences, Architecture, Modeling, and Best Practices

This article explains what a data warehouse is, contrasts it with traditional databases, outlines how to design and build a warehouse—including model selection, subject‑area definition, bus matrix, layering, and data quality—while also covering related concepts such as data middle platforms, data lakes, metadata, and modeling techniques.

Data ModelingData QualityETL
0 likes · 16 min read
Understanding Data Warehouses: Definitions, Differences, Architecture, Modeling, and Best Practices
IT Architects Alliance
IT Architects Alliance
Aug 31, 2021 · Databases

Why Graph Databases Matter: From Basics to Neo4j vs JanusGraph

The article explains the rapid rise of graph databases, outlines their core concepts and advantages, compares them with NoSQL and relational databases, presents performance benchmarks, and reviews leading solutions such as Neo4j and JanusGraph, including their data models and query language.

CypherData ModelingJanusGraph
0 likes · 10 min read
Why Graph Databases Matter: From Basics to Neo4j vs JanusGraph
ITPUB
ITPUB
Aug 30, 2021 · Databases

Why Graph Databases Are Revolutionizing Data Relationships: Neo4j vs JanusGraph

This article explains the rise of graph databases, compares them with traditional relational and NoSQL systems, details the differences between Neo4j and JanusGraph, and demonstrates how the Cypher query language enables efficient relationship queries in complex, large‑scale data environments.

CypherData ModelingJanusGraph
0 likes · 12 min read
Why Graph Databases Are Revolutionizing Data Relationships: Neo4j vs JanusGraph
Baidu Geek Talk
Baidu Geek Talk
Aug 25, 2021 · Databases

Applying Graph Database Technology to Baidu Chinese Dictionary Service

To meet Baidu Chinese’s need for sub‑200 ms responses on multi‑hop queries across millions of dictionary entities, the team replaced MySQL with the open‑source HugeGraph graph database backed by RocksDB, deploying a multi‑master, REST‑enabled architecture with caching, bulk loading, and a data‑intervention platform to ensure fast, reliable traversal of semantic relationships.

Baidu ChineseData ModelingGremlin
0 likes · 12 min read
Applying Graph Database Technology to Baidu Chinese Dictionary Service
DataFunTalk
DataFunTalk
Aug 23, 2021 · Artificial Intelligence

Graph Data Analysis and Graph Neural Network Applications Across Multiple Scenarios

This article introduces graph fundamentals, various application scenarios such as science, code logic, Spark workflows, social networks, and event graphs, then details graph data modeling, analysis, matrix computations, and the deployment of graph neural networks using frameworks like DGL, highlighting practical engineering considerations.

AIDGLData Modeling
0 likes · 16 min read
Graph Data Analysis and Graph Neural Network Applications Across Multiple Scenarios
DataFunSummit
DataFunSummit
Aug 22, 2021 · Big Data

Evolution and Optimization of Meituan Waimai Offline Data Warehouse: Architecture, ETL, Modeling, Governance, and Future Plans

This article details the historical development, architectural layers, ETL migration to Spark, data modeling standards, governance processes, resource optimization, security measures, and future roadmap of Meituan Waimai's offline data warehouse, illustrating how the team addressed scalability and efficiency challenges.

Data ModelingETLMeituan
0 likes · 21 min read
Evolution and Optimization of Meituan Waimai Offline Data Warehouse: Architecture, ETL, Modeling, Governance, and Future Plans
IT Architects Alliance
IT Architects Alliance
Jul 20, 2021 · Big Data

Understanding Data Middle Platform: Layers, Architecture, and Implementation Methodology

The article explains the concept of a data middle platform, detailing its three-layer structure—data model, data service, and data development—illustrates how data modeling enables cross-domain integration, how services encapsulate data for flexible consumption, and how development tools support customized data applications, using a telecom operator example.

Data Modelingbig datadata architecture
0 likes · 2 min read
Understanding Data Middle Platform: Layers, Architecture, and Implementation Methodology
Big Data Technology & Architecture
Big Data Technology & Architecture
Jun 6, 2021 · Big Data

Understanding Data Warehouses: Concepts, Architecture, Modeling, and Governance

This article provides a comprehensive overview of data warehouses, explaining their purpose, differences from databases, OLTP vs OLAP, traditional versus internet data warehouse models, layered architecture, modeling theories, metric dictionaries, date dimensions, naming conventions, data governance, and incremental synchronization techniques with practical SQL examples.

Data ModelingETLSQL
0 likes · 24 min read
Understanding Data Warehouses: Concepts, Architecture, Modeling, and Governance
Alibaba Cloud Native
Alibaba Cloud Native
May 24, 2021 · Operations

How to Build a Data‑Driven Stability Assurance System for Kubernetes Clusters

This article presents a systematic, data‑model‑driven approach to Kubernetes stability assurance, detailing the sources of complexity, a four‑diagram and three‑table data model, insight and pre‑plan structures, global visualisation concepts, deployment patterns, operational workflows, and competitive analysis to enable effective, iterative, and sustainable cluster stability management.

Data ModelingKubernetesincident management
0 likes · 15 min read
How to Build a Data‑Driven Stability Assurance System for Kubernetes Clusters
Java High-Performance Architecture
Java High-Performance Architecture
May 14, 2021 · Backend Development

Why CQRS? Unlocking Flexible Queries and Scalable Architecture

The article explains the CQRS (Command Query Responsibility Segregation) pattern, showing how separating command and query models addresses diverse data view needs, improves query flexibility, discusses synchronization methods, compares with microservices and caching, and outlines its benefits and trade‑offs.

CQRSCommand Query Responsibility SegregationData Modeling
0 likes · 5 min read
Why CQRS? Unlocking Flexible Queries and Scalable Architecture
TAL Education Technology
TAL Education Technology
Mar 18, 2021 · Artificial Intelligence

AI‑Driven Data Modeling for Optimizing Offline Advertising in Education OMO: A Comprehensive Case Study

This article presents a comprehensive case study on how an education company leverages AI‑driven data modeling and multi‑source geographic and demographic data to optimize offline advertising placement within its OMO strategy, detailing background, methodology, technical implementation, challenges, and future outlook.

AIData ModelingEducation
0 likes · 11 min read
AI‑Driven Data Modeling for Optimizing Offline Advertising in Education OMO: A Comprehensive Case Study
vivo Internet Technology
vivo Internet Technology
Mar 10, 2021 · Big Data

Path Analysis Model Design and Engineering Implementation for Internet Data Operations

The article details the design and engineering of a high‑performance path analysis model for internet data operations, explaining session handling, Sankey visualizations, adjacency‑table storage, multi‑granular session partitioning, Spark‑to‑ClickHouse pipelines, and optimizations that enable billion‑scale user‑path queries in about one second.

ClickHouseData ModelingOLAP
0 likes · 21 min read
Path Analysis Model Design and Engineering Implementation for Internet Data Operations
JD Tech Talk
JD Tech Talk
Feb 5, 2021 · Big Data

Design and Implementation of a Real‑Time OLAP Engine Using ClickHouse in JD Energy Management Platform

This article describes how JD's Energy Management Platform leverages ClickHouse as a high‑performance, MPP‑based OLAP engine to provide real‑time, multi‑dimensional analytics on IoT energy data, covering business background, technology selection, system architecture, data ingestion, storage, replication, and a generic query interface with code examples.

ClickHouseData ModelingKafka
0 likes · 11 min read
Design and Implementation of a Real‑Time OLAP Engine Using ClickHouse in JD Energy Management Platform
Top Architect
Top Architect
Jan 25, 2021 · Backend Development

GraphQL Overview: Concepts, Advantages over REST, and Architectural Patterns

This article explains the limitations of traditional REST APIs, introduces GraphQL as a flexible alternative, details its core concepts such as schema, types, queries, mutations and subscriptions, and outlines various deployment architectures and implementation considerations for backend development.

APIBackendData Modeling
0 likes · 14 min read
GraphQL Overview: Concepts, Advantages over REST, and Architectural Patterns
Architect
Architect
Dec 27, 2020 · Big Data

Optimizing Billion‑Scale Hive Queries: Partitioning, Indexing, Bucketing, Active‑User Segmentation, and Data Structure Refactoring

This article walks through the challenges of querying a 300‑billion‑row Hive table, analyzes why traditional partitioning, indexing, and bucketing fall short, and presents a practical solution that combines active‑user segmentation and a redesigned array‑based data model to cut query time from hours to minutes.

Data ModelingData PartitioningHive
0 likes · 10 min read
Optimizing Billion‑Scale Hive Queries: Partitioning, Indexing, Bucketing, Active‑User Segmentation, and Data Structure Refactoring
DataFunTalk
DataFunTalk
Dec 19, 2020 · Big Data

Evolution of iQIYI Data Warehouse from 1.0 to 2.0: Architecture, Modeling Practices, and Future Directions

This article details iQIYI's transition from a fragmented Data Warehouse 1.0 to a unified, standardized Data Warehouse 2.0, covering layered architecture, dimension and metric design, modeling workflows, metadata management, data lineage, and upcoming intelligent and automated data platform initiatives.

Data Modelingdata lineagedata warehouse
0 likes · 25 min read
Evolution of iQIYI Data Warehouse from 1.0 to 2.0: Architecture, Modeling Practices, and Future Directions
NetEase Yanxuan Technology Product Team
NetEase Yanxuan Technology Product Team
Nov 27, 2020 · Product Management

How to Build Effective Decision‑Making Products: A Practical Blueprint

This article outlines a comprehensive framework for designing decision‑type products, covering their evolution stages, core elements of model‑data‑strategy, domain modeling techniques, data‑to‑knowledge transformation, business and process value, and a feedback‑driven decision loop with evaluation and simulation.

Business AnalyticsData ModelingDecision Products
0 likes · 20 min read
How to Build Effective Decision‑Making Products: A Practical Blueprint
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 13, 2020 · Big Data

Evolution of iQIYI Data Warehouse from 1.0 to 2.0: Architecture, Modeling, Metadata, and Data Lineage

The talk chronicles iQIYI’s shift from a fragmented five‑layer Data Warehouse 1.0 to a unified 2.0 architecture featuring a central Dimension Layer, business‑focused data marts, and subject‑oriented warehouses, while detailing platform services, rigorous metadata management, lineage tracking, and future goals of intelligent, automated, service‑oriented, model‑driven data governance.

Data Modelingdata lineageiQIYI
0 likes · 23 min read
Evolution of iQIYI Data Warehouse from 1.0 to 2.0: Architecture, Modeling, Metadata, and Data Lineage
Architecture Digest
Architecture Digest
Nov 5, 2020 · Databases

Elasticsearch Interview Question: Performance Optimization and Best Practices

The article explains how to improve Elasticsearch query speed on billions of records by leveraging filesystem cache, reducing indexed fields, using data pre‑heating, separating hot and cold indices, designing efficient document models, and applying pagination techniques such as scroll API and search_after.

BackendData ModelingFilesystem Cache
0 likes · 11 min read
Elasticsearch Interview Question: Performance Optimization 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 ModelingHTAP
0 likes · 21 min read
Understanding OLAP vs. OLTP and the Fundamentals of Data Warehousing
政采云技术
政采云技术
Aug 30, 2020 · Frontend Development

High-Quality Maintainable Code: Data Modeling for Frontend Development

This article explains the fundamentals of data modeling for front‑end development, covering its three core elements, integrity constraints, domain‑driven design, layered architecture, and practical guidelines for building clean, maintainable front‑end data models.

Data ModelingDomain-Driven Designfrontend
0 likes · 14 min read
High-Quality Maintainable Code: Data Modeling for Frontend Development
Programmer DD
Programmer DD
Aug 22, 2020 · Backend Development

Why Elasticsearch Can Be Slow and How to Supercharge Its Performance

This article examines common Elasticsearch interview questions, explains why initial searches can be slow, and provides practical strategies such as leveraging filesystem cache, data pre‑heating, cold‑hot index separation, minimal document design, and scroll or search_after APIs to dramatically improve search performance and pagination efficiency.

Data ModelingElasticsearchFilesystem Cache
0 likes · 13 min read
Why Elasticsearch Can Be Slow and How to Supercharge Its Performance
Big Data Technology & Architecture
Big Data Technology & Architecture
Aug 16, 2020 · Big Data

Designing a Reusable and Standardized Data Warehouse Model for a Data Middle Platform

The article explains how to evaluate and improve data warehouse design by measuring completeness, reusability and standardization, proposes concrete metrics such as cross‑layer reference rate and model reuse coefficient, and outlines a step‑by‑step process—from ODS control to domain division, dimension unification, fact‑table integration, model development and migration—while introducing the EasyDesign tool for systematic management.

Data Modelingdata platformgovernance
0 likes · 14 min read
Designing a Reusable and Standardized Data Warehouse Model for a Data Middle Platform
Ctrip Technology
Ctrip Technology
Aug 6, 2020 · Big Data

Data Governance Practices and Model Design in Ctrip Vacation Data Warehouse

This article shares the practical experience and thinking behind Ctrip's vacation data governance project, covering team efficiency optimization, demand sorting, data domain definition, warehouse layering, unified dimension modeling, metric standardization, and the overall benefits of a centralized data governance framework.

CtripData Modelingbig data
0 likes · 17 min read
Data Governance Practices and Model Design in Ctrip Vacation Data Warehouse
Xianyu Technology
Xianyu Technology
Aug 6, 2020 · Product Management

Xianyu Product Understanding and Quality Item Selection Framework

Xianyu’s new quality‑item framework evaluates each listing through product, supply‑demand, price, and seller attributes to compute a quality score, optimizes a balanced pool of high‑quality items, and—validated by back‑testing and simulations—boosts click‑through and payment conversion rates dramatically.

Data ModelingOptimizatione-commerce
0 likes · 8 min read
Xianyu Product Understanding and Quality Item Selection Framework
Ctrip Technology
Ctrip Technology
Jul 30, 2020 · Backend Development

Design and Implementation of Tripyun Ctrip Cloud Metadata and SPU Management Platform

This article describes the background, metadata concepts, SPU and category‑attribute modeling, MySQL tree‑storage techniques, dynamic business configuration, rule‑engine and workflow‑engine solutions adopted in Tripyun‑Ctrip Cloud to build a reusable, scalable middle‑platform for the tourism industry.

BackendData ModelingRule Engine
0 likes · 21 min read
Design and Implementation of Tripyun Ctrip Cloud Metadata and SPU Management Platform
Tencent Cloud Developer
Tencent Cloud Developer
Jul 13, 2020 · Big Data

Building MVP: A Lightweight Big Data Analysis System for Product Growth

The article describes how a lightweight big‑data analysis platform called MVP was built from scratch—using a User‑Event‑Config model, HDFS + ClickHouse + Spark, and four modules for metric monitoring, root‑cause alerts, deep growth analysis, and A/B testing—enabling real‑time insights in seconds instead of days and dramatically accelerating product‑growth operations.

AARRR ModelClickHouseData Modeling
0 likes · 9 min read
Building MVP: A Lightweight Big Data Analysis System for Product Growth
Architecture Digest
Architecture Digest
Jul 8, 2020 · Backend Development

Understanding GraphQL: Advantages Over REST and Core Concepts

This article explains the limitations of traditional REST APIs, introduces GraphQL as a flexible alternative, and details its core concepts, type system, server‑client architectures, deployment options, and popular tools and services for modern backend development.

API DesignData ModelingGraphQL
0 likes · 14 min read
Understanding GraphQL: Advantages Over REST and Core Concepts
Big Data Technology Architecture
Big Data Technology Architecture
Jul 8, 2020 · Big Data

Key Interview Questions on Data Warehousing, Data Platforms, and Related Technologies

This article compiles a comprehensive set of 32 interview questions covering data warehouse fundamentals, data platform construction, modeling approaches, real‑time architectures, data quality, governance, Hive optimization, and related analytical techniques to help candidates prepare for data engineering roles.

Data ModelingETLdata platform
0 likes · 4 min read
Key Interview Questions on Data Warehousing, Data Platforms, and Related Technologies
Architects Research Society
Architects Research Society
Jun 15, 2020 · Databases

Overview of Data Modeling, Architecture, Master Data Management, Metadata, and Data Quality

This article explains the concepts of data modeling and architecture, including logical data, process, and rule modeling, various data model types, master data management principles, metadata categories, and data quality management practices, highlighting their roles in enterprise information systems.

Data ModelingData QualityMaster Data Management
0 likes · 9 min read
Overview of Data Modeling, Architecture, Master Data Management, Metadata, and Data Quality
JD Tech Talk
JD Tech Talk
Jun 5, 2020 · Information Security

From System Engineering to Anti‑Black‑Market: Lin Yuansheng’s Journey in JD’s Intelligent Risk Detection

Lin Yuansheng, JD’s first post‑doctoral researcher, leverages his system‑engineering background and big‑data analytics to build models that identify black‑market accounts, improve network risk perception, and demonstrate how academic research can be rapidly applied to real‑world e‑commerce security challenges.

Data Modelingrisk detectionsystem engineering
0 likes · 8 min read
From System Engineering to Anti‑Black‑Market: Lin Yuansheng’s Journey in JD’s Intelligent Risk Detection
dbaplus Community
dbaplus Community
Jun 2, 2020 · Big Data

How Cainiao Built a Scalable Real‑Time Data Warehouse with Flink

Facing growing order volumes and strict timeliness demands, Cainiao’s tech team overhauled its real‑time data warehouse by redesigning data models, adopting Flink for streaming computation, upgrading data services, and exploring innovative tools, sharing practical lessons and future directions for large‑scale logistics analytics.

Data ModelingFlinkStreaming
0 likes · 18 min read
How Cainiao Built a Scalable Real‑Time Data Warehouse with Flink
ITPUB
ITPUB
Jun 1, 2020 · Databases

How Ant Financial Scales Payments: Data Patterns and Disaster‑Recovery Strategies

This article examines how Ant Financial redesigned its payment system’s data layer after retiring mainframes, detailing RTO/RPO goals, CAP trade‑offs, vertical and horizontal sharding, blacklist‑based accounting DR, failover mechanisms for transactions, and the role of OceanBase in achieving strong consistency and low‑latency recovery.

Data ModelingFinancial Technologydatabase sharding
0 likes · 17 min read
How Ant Financial Scales Payments: Data Patterns and Disaster‑Recovery Strategies
Architects Research Society
Architects Research Society
May 25, 2020 · Databases

Concern-Driven Data Architecture and Full-Scale Schema Modeling

The article explores a concern‑driven approach to data architecture, introducing a full‑scale schema modeling framework that balances data control, flexibility, push/pull delivery, and governance, and discusses multi‑level concerns, property‑graph standards, and practical scenarios for evolving schemas in complex business contexts.

Data ModelingProperty Graphconcern-driven
0 likes · 18 min read
Concern-Driven Data Architecture and Full-Scale Schema Modeling
Efficient Ops
Efficient Ops
May 20, 2020 · Operations

How to Build a Sustainable CMDB: Three Essential Phases for Reliable Operations

This article explains how to design, implement, and maintain a robust Configuration Management Database (CMDB) by focusing on simple modeling, establishing data closure loops, and efficiently handling existing inventory, while leveraging Kafka, Flink, Elasticsearch, and Neo4j for fast querying and topology visualization.

CMDBData Modelingautomation
0 likes · 19 min read
How to Build a Sustainable CMDB: Three Essential Phases for Reliable Operations
HomeTech
HomeTech
May 20, 2020 · Big Data

AutoHome Data Warehouse Architecture and Layered Model Design

This article describes AutoHome's data warehouse architecture, detailing its background, business pain points, layered model design (RDM, ADM, GDM, FDM, Stage/BDM, DIM, TMP), advantages in performance, cost, efficiency, quality, and various application scenarios including BI, analytics, and decision support.

BIData ModelingETL
0 likes · 10 min read
AutoHome Data Warehouse Architecture and Layered Model Design
DataFunTalk
DataFunTalk
May 14, 2020 · Big Data

Building a Real-Time Data Warehouse at Cainiao: Architecture, Model Upgrades, Engine Enhancements, and Service Innovations

This article shares Cainiao's practical experience in constructing a real-time data warehouse, covering the shortcomings of the previous architecture, the evolution of data models, the migration to Flink with advanced features like retraction and timer services, and the modernization of data services and tooling to support high‑throughput logistics scenarios.

Data ModelingData ServiceFlink
0 likes · 16 min read
Building a Real-Time Data Warehouse at Cainiao: Architecture, Model Upgrades, Engine Enhancements, and Service Innovations
WecTeam
WecTeam
Apr 8, 2020 · Frontend Development

How MPM Revolutionizes H5 Page Data Modeling for Efficient Multi‑Platform Rendering

This article details how JD's MPM visual H5 page builder tackles complex data‑request challenges by introducing a unified data model, request aggregation, and three‑end rendering support, offering a scalable solution that improves performance, maintainability, and developer experience across web, app, and server‑side rendering.

Data ModelingH5MPM
0 likes · 23 min read
How MPM Revolutionizes H5 Page Data Modeling for Efficient Multi‑Platform Rendering
360 Quality & Efficiency
360 Quality & Efficiency
Mar 24, 2020 · Big Data

Understanding Granularity in Data Warehouse Design

This article explains the concept of granularity in data warehouse design, describing data models composed of structures, operations, and constraints, illustrating how granularity affects storage detail, query performance, and resource consumption, and recommending a dual‑granularity approach to balance efficiency and analytical depth.

AnalyticsData Modelingbig data
0 likes · 5 min read
Understanding Granularity in Data Warehouse Design
Big Data Technology Architecture
Big Data Technology Architecture
Feb 15, 2020 · Databases

An Introduction to HBase: Architecture, Data Model, Storage Engine, Indexing, Features, and Use Cases

This article provides a comprehensive overview of HBase, covering its LSM‑Tree based storage engine, key‑value data model, column‑family storage design, indexing mechanisms, major advantages and drawbacks, and typical scenarios where HBase excels for massive, high‑throughput data workloads.

Data ModelingHBaseIndexing
0 likes · 8 min read
An Introduction to HBase: Architecture, Data Model, Storage Engine, Indexing, Features, and Use Cases
Big Data Technology Architecture
Big Data Technology Architecture
Feb 13, 2020 · Big Data

Evolution of Cainiao's Real-Time Data Warehouse Architecture: Model, Compute Engine, and Data Service Upgrades

The talk details Cainiao’s evolution of its real‑time data warehouse architecture, covering the original 2016 model, compute and service challenges, the 2017 multi‑layer data model redesign, migration to Flink, practical cases of state retraction, timeout statistics, smart optimizations, and the unified data service platform.

Data ModelingData ServiceFlink
0 likes · 16 min read
Evolution of Cainiao's Real-Time Data Warehouse Architecture: Model, Compute Engine, and Data Service Upgrades
58 Tech
58 Tech
Feb 10, 2020 · Big Data

Construction and Practice of a Site-wide User Behavior Data Warehouse at 58.com

This article systematically describes the challenges, design principles, modeling methods, layered architecture, implementation steps, and standards used in building a comprehensive user behavior data warehouse for 58.com, highlighting practical experiences and future improvement directions.

Data ModelingData QualityETL
0 likes · 11 min read
Construction and Practice of a Site-wide User Behavior Data Warehouse at 58.com
Aikesheng Open Source Community
Aikesheng Open Source Community
Dec 26, 2019 · Databases

Database Design: From ER Modeling to Normalization

This article explains the importance of good database design, walks through the stages of building an ER conceptual model, shows how to map entities, attributes, and relationships to relational schemas, and demonstrates how normalization (1NF, 2NF, 3NF, BCNF) improves schema quality using practical examples.

Data ModelingER Modelingdatabase design
0 likes · 17 min read
Database Design: From ER Modeling to Normalization
vivo Internet Technology
vivo Internet Technology
Dec 18, 2019 · Big Data

Comprehensive Overview of Big Data Architecture, Lambda/Kappa Models, and End-to-End Data Platform Design

The article surveys modern big‑data architecture, contrasting Lambda and Kappa models, highlights common governance and integration pain points, and proposes an end‑to‑end platform featuring unified metadata, stream‑batch processing, one‑click ingestion, standardized modeling, intelligent query abstraction, and a comprehensive development IDE.

Data ModelingETLKappa Architecture
0 likes · 13 min read
Comprehensive Overview of Big Data Architecture, Lambda/Kappa Models, and End-to-End Data Platform Design
Big Data Technology & Architecture
Big Data Technology & Architecture
Nov 13, 2019 · Databases

ClickHouse Engines: Use Cases, Syntax, and Limitations

This article provides a comprehensive overview of ClickHouse, covering its typical application scenarios, inherent limitations, common SQL syntax, default values, data types, materialized and expression columns, and detailed explanations of its various storage engines such as TinyLog, Log, Memory, Merge, Distributed, Null, Buffer, Set, MergeTree, ReplacingMergeTree, SummingMergeTree, AggregatingMergeTree, and CollapsingMergeTree, accompanied by practical code examples.

ClickHouseData ModelingDatabase Engines
0 likes · 25 min read
ClickHouse Engines: Use Cases, Syntax, and Limitations
System Architect Go
System Architect Go
Oct 27, 2019 · Databases

Mastering InfluxDB: Data Types, Schema Design, and Index Configuration

This guide explains InfluxDB’s schemaless data types, best practices for designing tag‑and‑field schemas, and how to choose and configure its in‑memory or TSI indexes, including key parameters such as max‑series‑per‑database and max‑values‑per‑tag for optimal performance.

ConfigurationData ModelingIndexing
0 likes · 6 min read
Mastering InfluxDB: Data Types, Schema Design, and Index Configuration
System Architect Go
System Architect Go
Oct 25, 2019 · Databases

Understanding InfluxDB: Core Concepts, Architecture, and Design Trade‑offs of a Time‑Series Database

This article explains the fundamentals of time‑series databases, introduces InfluxDB’s key components such as databases, measurements, retention policies, tags, fields, series, and points, describes the line protocol syntax, compares it with traditional databases, and outlines the design trade‑offs that shape its performance and limitations.

Data ModelingInfluxDBLine Protocol
0 likes · 10 min read
Understanding InfluxDB: Core Concepts, Architecture, and Design Trade‑offs of a Time‑Series Database
Meituan Technology Team
Meituan Technology Team
Oct 17, 2019 · Big Data

OneData Methodology: Building a Unified Data Warehouse Architecture and Governance Framework

By adapting Alibaba’s OneData methodology, the project establishes a unified data‑warehouse architecture, standards, and governance framework—including consolidated business intake, standardized design layers, naming conventions, and delivery metrics—that resolves data‑quality issues, enhances scalability and reusability, and delivers faster, reliable data support for evolving business needs.

Data ModelingOneDataStandardization
0 likes · 15 min read
OneData Methodology: Building a Unified Data Warehouse Architecture and Governance Framework
Sohu Tech Products
Sohu Tech Products
Oct 17, 2019 · Databases

HBase Table Design Strategies and Best Practices

This article explains HBase's data model and key components, details column descriptor options such as BloomFilter, Compression, Versions, TTL, and MinVersion, and provides practical design guidelines for columns, rowkeys, high vs. wide tables, region pre‑splitting, and hotspot mitigation to achieve optimal performance.

Data ModelingHBaseNoSQL
0 likes · 17 min read
HBase Table Design Strategies and Best Practices
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 12, 2019 · Big Data

How Alibaba Turned a Mall into a Fully Digital IoT Ecosystem

This article details how Alibaba engineers built a digital mall by defining a unified device data dictionary, deploying Niagara IoT, implementing edge‑computing with IBOS, constructing a layered data‑center architecture, and delivering analytics, visualization, and service platforms for traffic, transaction, and membership insights.

Data ModelingDigital TwinIoT
0 likes · 13 min read
How Alibaba Turned a Mall into a Fully Digital IoT Ecosystem
Mafengwo Technology
Mafengwo Technology
Sep 26, 2019 · Big Data

Mafengwo’s Data Warehouse & Middle Platform: Architecture, Modeling, Toolchain

This article details Mafengwo’s journey in constructing a data warehouse and data middle platform, covering the core three‑layer architecture, hybrid modeling approaches, the supporting toolchain for data synchronization, scheduling, and metadata management, and the design of an indicator platform for business analytics.

Big Data ArchitectureData ModelingETL
0 likes · 18 min read
Mafengwo’s Data Warehouse & Middle Platform: Architecture, Modeling, Toolchain
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 27, 2019 · Backend Development

Building a Simple IM System with Alibaba Tablestore Timeline: A Step‑by‑Step Guide

This article explains how to model and implement a basic instant‑messaging system using Alibaba Tablestore's Timeline model, covering message storage, relationship management, metadata handling, real‑time notification, multi‑device synchronization, and provides complete Java code examples for each functional module.

BackendData ModelingJava
0 likes · 23 min read
Building a Simple IM System with Alibaba Tablestore Timeline: A Step‑by‑Step Guide
21CTO
21CTO
Jul 11, 2019 · Big Data

Boost Elasticsearch Queries on Billions of Docs: Filesystem Cache & Smart Design

Elasticsearch performance at billions‑scale can be dramatically improved by leveraging the OS filesystem cache, limiting indexed fields, separating hot and cold data, pre‑warming caches, and using scroll or search_after for pagination, while avoiding costly joins and ensuring the dataset fits in memory.

Data ModelingElasticsearchFilesystem Cache
0 likes · 12 min read
Boost Elasticsearch Queries on Billions of Docs: Filesystem Cache & Smart Design
Architecture Digest
Architecture Digest
May 28, 2019 · Backend Development

Improving Elasticsearch Query Performance for Billion‑Scale Datasets

To boost Elasticsearch query speed on billions of records, allocate sufficient filesystem cache memory, store only searchable fields, separate hot and cold data, warm up cache, avoid complex joins, and replace deep pagination with Scroll API or search_after for millisecond‑level responses.

Data ModelingElasticsearchFilesystem Cache
0 likes · 10 min read
Improving Elasticsearch Query Performance for Billion‑Scale Datasets
dbaplus Community
dbaplus Community
May 21, 2019 · Big Data

How to Supercharge Elasticsearch Queries on Billions of Records

This article explains why Elasticsearch can be slow on massive datasets, then details practical techniques—leveraging filesystem cache, pre‑heating hot data, separating hot and cold indices, designing lean document models, and avoiding deep pagination—to achieve sub‑second query performance at billions‑scale.

Data ModelingElasticsearchbig data
0 likes · 11 min read
How to Supercharge Elasticsearch Queries on Billions of Records
Senior Brother's Insights
Senior Brother's Insights
Apr 20, 2019 · Databases

Essential MySQL Database Design and Coding Standards for Reliable Systems

This guide outlines comprehensive MySQL naming conventions, table and column design rules, indexing best practices, SQL coding standards, and operational recommendations to improve performance, maintainability, and security while avoiding common pitfalls such as redundant indexes, unsafe data types, and excessive batch operations.

Best PracticesData ModelingIndexing
0 likes · 19 min read
Essential MySQL Database Design and Coding Standards for Reliable Systems
JD Tech
JD Tech
Feb 18, 2019 · Big Data

Understanding HBase: Advantages, Use Cases, Data Model, and Architecture

This article explains HBase as a high‑performance, column‑oriented distributed storage system, outlines its advantages and limitations, presents real‑world scenarios such as seller operation logs and message logs, and details its data structures, architecture components, and design considerations for big‑data applications.

Data ModelingHBaseNoSQL
0 likes · 9 min read
Understanding HBase: Advantages, Use Cases, Data Model, and Architecture
ITPUB
ITPUB
Jan 10, 2019 · Databases

Mastering Neo4j: From Graph Modeling to Advanced Cypher Queries

This comprehensive guide explains Neo4j's label‑property graph model, node and relationship creation, Cypher syntax, indexing, constraints, schema inspection, and best practices for avoiding duplicate data, providing practical examples and performance tips.

CypherData ModelingIndexing
0 likes · 29 min read
Mastering Neo4j: From Graph Modeling to Advanced Cypher Queries
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 31, 2018 · Artificial Intelligence

How Alibaba Built an E‑commerce Knowledge Graph to Power Smarter Search

This article explains Alibaba’s end‑to‑end approach to constructing an e‑commerce knowledge graph—detailing the background, challenges, data‑structuring methods, schema design, modular architecture, and deployment pipeline that enable deep user‑intent understanding across complex shopping scenarios.

AIData ModelingOntology
0 likes · 13 min read
How Alibaba Built an E‑commerce Knowledge Graph to Power Smarter Search