This article explains the four major NoSQL data models—key‑value, document, column‑family, and graph—detailing their structures, strengths, weaknesses, CAP trade‑offs, consistency options, and how to match each model to specific business queries and requirements, including polyglot persistence strategies.
The article explains why data modeling is the bridge between business processes and analytical applications, breaks down eight common modeling methods—including ER, 3NF, dimensional, star, snowflake, wide‑table, Data Vault, and subject‑oriented models—detailing their purposes, strengths, trade‑offs, and how they fit into a layered data‑warehouse architecture.
Many enterprises with ERP, CRM and other systems mistakenly treat a data warehouse as just a larger database, but the article explains that databases support transaction processing while data warehouses provide a stable, unified analytics layer, detailing their distinct goals, architectures, layer designs and the critical role of reliable data integration.
Many companies start data‑warehouse projects by merely extracting data, building a few tables and adding a BI report, only to face inconsistent metrics, unreadable tables, mismatched numbers and endless Excel rechecks; the article outlines a full end‑to‑end process—from source‑data inventory and stable ingestion to layered storage, modeling, metric unification and quality monitoring—to ensure trustworthy, reusable analytics.
Data modeling isn’t about creating more tables; the real challenge lies in translating business logic, relationships, and metric definitions into a stable structure, which requires mastering seven key concepts—business objects, granularity, entity relationships, fact and dimension tables, metric definitions, model layering, and data lineage and quality.
The article outlines a practical, four‑step process for architects to fully understand and redesign an aging legacy system, covering business knowledge, the four core functional modules, comprehensive data‑model analysis, and handling of business exceptions, while noting the limited role of AI assistance.
Many enterprises struggle to extract business insights because their underlying data is chaotic, definitions are inconsistent, and metric definitions clash, so even powerful analysis tools cannot deliver value until they clearly separate data models from metric models and follow systematic building steps.
The article breaks down data modeling into three essential layers—conceptual, logical, and physical—showing how each layer clarifies business rules, structures data, and translates designs into performant database implementations, thereby strengthening data governance and AI initiatives.
The article explains why data architects have become essential in the DT era, detailing their responsibilities, core skills, big‑data technology stack, governance practices, career paths, and the tools they use to turn data into a strategic asset for enterprises.
This article walks through eight fundamental data modeling methods—regression, classification, clustering, PCA, factor analysis, association rules, time‑series, and cluster analysis—detailing their typical use cases, core logic, key considerations, and practical tips for effective implementation.
This article walks readers through the entire data‑modeling lifecycle—from clarifying business goals and inventorying data sources to defining metrics, designing schema, processing data, validating quality, and iterating the model for sustainable business impact.
This article introduces Neo4j, explains how graph databases differ from relational tables, walks through three installation methods, details nodes, relationships, and paths, provides extensive Cypher examples for creating, querying, updating, and deleting data, and shows how to integrate Neo4j with Spring Boot, plus pros, cons, and use cases.
Data modeling, far beyond simple table design, provides a comprehensive framework that aligns business objects, relationships, metrics, and data flow, enabling accurate data, efficient development, and smooth collaboration; the article explains concepts, types, methods, and step‑by‑step practices, and highlights integration tools like FineDataLink.
The article analyzes how Claude large‑model AI is applied to DeWu’s financial data warehouse, detailing the domain’s unique challenges, the model’s three core capabilities, practical use‑cases such as OneData standardised modelling, AI‑assisted SQL coding and automated data testing, and the resulting efficiency, quality and reusability gains.
This playbook provides enterprise‑level data warehouse engineers, ETL developers, data modelers, and data‑team managers with a complete, logical, and actionable set of standards, processes, and best‑practice guidelines covering architecture, development principles, role responsibilities, end‑to‑end workflow, metadata, security, performance metrics, and team collaboration.
In a 90‑minute round‑table, experts from banking risk control and cloud observability explain how Palantir’s ontology—viewed as the skeleton and memory that structures massive, heterogeneous data—bridges three data gaps, enables large‑model reasoning, and offers concrete steps for building practical knowledge graphs in enterprises.
In a 90‑minute round‑table, experts from banking risk control and cloud observability explain how Palantir’s ontology bridges three data gaps, turns raw logs into a graph of entities and relationships, and works with large models as a skeleton and memory to make AI trustworthy and scalable.
The article explains how ODS serves as a short‑term, source‑aligned store for near‑real‑time data, Data Marts provide department‑focused, lightly aggregated datasets, and Data Warehouses act as the central, integrated, non‑volatile repository, comparing modeling styles and architectural approaches.
Healthpeak, a large medical‑real‑estate REIT, replaced fragmented spreadsheets and manual data entry with Palantir AIP’s ontology‑driven AI operating system, achieving automated billing, voice‑driven workflows, reduced errors, and a scalable, data‑centric operation that frees managers to focus on tenant relationships.
In a 90‑minute round‑table hosted by DataFun, experts from banking risk control and cloud observability dissect how Palantir’s ontology—structured as a graph that links entities, metrics and logs—complements large‑model AI, solves data chaos, and becomes the practical backbone for trustworthy enterprise AI.
In a 90‑minute round‑table hosted by DataFun, experts from Shanghai Bank, Alibaba Cloud, and academia dissect how ontology bridges data chaos, model opacity, and engineering scale, enabling trustworthy AI for financial risk control and cloud observability while outlining practical steps for building usable knowledge graphs.
The article explains that a data warehouse is an analysis‑oriented data management system that integrates, cleans, and stores enterprise data to provide a single, trustworthy source for reporting and decision‑making, outlines common data problems, and presents a step‑by‑step methodology—including goal definition, source integration, layering, governance, and BI connection—to successfully build and maintain one.
The article explains that most data‑quality issues stem from unclear definitions, outlines what data standard management is, describes the three layers of organizational data, details seven categories of data standards, and provides a step‑by‑step guide for creating, publishing, implementing, evaluating, and continuously improving those standards.
This article explains the fundamental differences between fact tables and dimension tables in a data warehouse, covering their core contents, key characteristics, design best‑practices, types, how they interrelate, and maintenance tips for reliable analytics.
This article explains the purpose and benefits of data warehouse layering—ODS, DWD, DIM, DWS, DWT, and ADS—detailing each layer's responsibilities, design principles, storage options, and how strict layer calling conventions improve data traceability, reduce duplication, and simplify downstream analytics.
The article explains why solid data modeling is essential, then walks through four widely used techniques—normalization, dimensional modeling, Data Vault, and graph modeling—detailing their principles, typical use cases, advantages, and trade‑offs, and shows how they fit into layered data‑warehouse architectures.
This article walks through building a production‑grade system for storing and querying massive GPS trajectory data with MongoDB, covering data modeling choices, index strategies, sharding, query patterns, performance tuning, and best‑practice checklists to handle billions of points efficiently.
The article clarifies the distinction between data models, which structure and store business data, and metric models, which define and manage business measurements, covering their core elements, construction logic, outputs, application stages, iteration frequency, and how they complement each other.
This article provides a comprehensive, business‑driven analysis of the Data Warehouse Service (DWS) layer, covering its core positioning, design goals, modeling and aggregation tactics, storage optimizations, typical challenges with practical solutions, and best‑practice recommendations for building efficient, cost‑effective data services.
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.
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.
The article explains why rising data volumes inflate cloud bills, identifies poor data modeling as the root cause, and outlines five practical steps—field pruning, proper data layering, pre‑aggregation, hot/cold data segregation, and model documentation—that together can save a company hundreds of thousands of compute dollars each month while boosting query performance.
The article explains how legacy monitoring based on isolated metrics, traces, and logs cannot keep up with the massive, fragmented, and dynamic data of modern IT systems, and introduces UModel—a graph‑based observability model that bridges data, model, and engineering gaps to enable AI‑driven operations.
This article dissects the five most common pitfalls when transitioning from DWD to DWS aggregation tables—such as chimney‑style designs, over‑wide tables, grain mismatches, missing drill‑down keys, and performance neglect—and offers concrete, production‑ready solutions to create reusable, efficient, and cost‑effective data‑warehouse layers.
This article explains the concept, key terminology, and practical implementation of Vintage (cohort) analysis in financial services, detailing how to build tables and curves, integrate data pipelines, and use the insights to optimize marketing strategies, credit risk assessment, and operational efficiency.
When business requirements change every few days, traditional data architectures become rigid, tightly coupled, and slow, causing endless cycles of overtime; this article analyzes the root causes and presents a layered, configurable, and API‑driven approach—including moderate redundancy, data catalogs, and decoupled layers—to enable data systems to adapt quickly and reliably.
This article compiles the most frequently asked interview questions for 2026 data‑warehouse development engineers, covering core concepts, layer architecture, SQL optimization, window functions, Hive vs Spark, data skew solutions, modeling metrics, slowly changing dimensions, scheduling tools, data quality monitoring, and real project experience.
This article explains how dbt drives automated data modeling and governance, how DataOps practices bring agility and control to data projects, and how StarRocks’ lakehouse architecture enables real‑time and batch analytics, illustrated with concrete workflows, version‑control conventions, and enterprise case studies.
The article explains why inconsistent field definitions across systems stem from a lack of a unified data model, defines data modeling versus data model, outlines the three modeling stages, compares three common modeling approaches—normative, dimensional, and entity—and shows how proper modeling supports data governance, ETL, and analytics.
The article examines common data‑warehouse pitfalls—poor data quality, endless ad‑hoc queries, and unreadable SQL—then outlines a systematic approach that includes correcting mindset, designing a layered architecture, applying disciplined modeling practices, enforcing data governance, and managing engineering details for sustainable success.
The article explains why structured data modeling is essential for large‑scale systems, compares OLTP and OLAP approaches, details ER, dimensional, Data Vault and Anchor methodologies, and walks through Alibaba's multi‑stage data‑modeling practice, including OneData implementation, dimension design, fact‑table types, and aggregation strategies.
The article explains what a data dictionary is, why it is essential for collaboration, clarity, and security, and provides a step‑by‑step guide—including examples of data items, structures, flows, storage, and processing—to help teams create and maintain effective dictionaries.
This article explains why architecture‑level contract decisions are needed in distributed systems, compares strict and loose data contracts, illustrates schema‑on‑read/write patterns, and shows how to ensure forward and backward compatibility when evolving protocols such as JSON and Protobuf.
Effective data modeling is crucial for system stability, and this guide walks you through core principles, twelve common table patterns—from batch and log tables to hierarchical and bitmap structures—detailing design rules, trade‑offs, usage scenarios, and practical examples to help you avoid costly redesigns.
The article compares storing user objects in Redis using plain SET with JSON versus using HASH fields, providing code demos, benchmark results, memory and concurrency analysis, and practical guidelines on when to choose each approach for optimal performance and safety.
This article details the end‑to‑end technical redesign of an e‑commerce management‑category system, covering business pain points, a layered backend architecture, core Java modules, data‑model design, data‑warehouse computation, automated rule validation, approval workflows, and the resulting efficiency gains that shrink rule‑update cycles to just one or two days.
The article analyzes common pain points in data‑warehouse metric management from business, technical, and product perspectives, outlines unified goals, defines a comprehensive model—including data domains, dimensions, atomic, derived, and composite metrics—and presents a step‑by‑step process for designing, implementing, and governing a scalable metric system.
This article explains the distinct concepts of data domain and subject area, uses a library‑vs‑bookstore analogy, presents a real e‑commerce case, compares them in a concise table, and offers best‑practice steps and common pitfalls to help data teams design efficient data architectures.
This article details how HuoLala built a high‑performance user profiling platform on Apache Doris, redesigning data models, leveraging bitmap storage, and applying query‑level optimizations to achieve up to 30‑fold speed gains, lower memory usage, and scalable real‑time analytics.
This article introduces the core principles of Grey Relational Analysis and the Grey Prediction Model, explains their calculation steps, and demonstrates how they can be applied across engineering, economics, and environmental fields to analyze limited data, select key indicators, evaluate systems, and forecast trends.
This article outlines the fundamentals of banking data warehouses, compares them with ODS, data marts, and data lakes, and presents a detailed development workflow and management system that emphasizes standards, role division, and tool-supported automation for efficient, reliable data warehousing.
The article explains why a well‑designed data model is essential for data‑warehouse performance and reliability, then walks through three core modeling patterns—flat tables, master/detail, and dimensional (star) schemas—illustrating each with concrete Power BI examples, step‑by‑step design stages, and trade‑offs such as many‑to‑many relationships and bidirectional filters.
JSON has become the de‑facto format for data exchange, and modern relational databases like PostgreSQL and MySQL now support native JSON types alongside NoSQL solutions such as MongoDB, offering developers flexible storage, indexing, and query capabilities while balancing schema rigidity, performance, and scalability.
This article details how Kuaishou’s content middle platform tackled the massive challenges of real‑time, flexible, and cost‑effective data analysis at trillion‑scale by redesigning its architecture, adopting ClickHouse, splitting wide tables, and implementing a scatter‑gather execution model with pre‑shuffle and bitmap optimizations.
After a failed flatten‑field migration from MySQL to Elasticsearch caused incorrect product matches, the team introduced nested types, redesigned mappings, rewrote queries with nested and inner_hits, optimized performance, documented pitfalls, and concluded that nested types solve one‑to‑many relations but require careful evaluation.
This article uses everyday analogies—herbal shops, libraries, and refrigerators—to demystify databases, covering relational tables, primary/secondary keys, foreign keys, DBMS, and SQL in a clear, beginner‑friendly way.
This article compares MySQL and Elasticsearch across data models, query languages, indexing, distributed architecture, performance, scalability, and typical use cases, helping readers decide which system best fits their application requirements in modern software development.
This article shares a collection of PPT slides that showcase classic data architecture and data model design methods, offering valuable insights for building data middle platforms and master‑data‑driven governance solutions.
This article explains how Baidu’s search data team redesigned its data warehouse with wide‑table modeling, Parquet columnar storage, and a Spark‑ClickHouse fusion engine, eliminating redundancy, cutting query latency from minutes to seconds, and enabling self‑service analytics for thousands of users.
This guide walks through enterprise data architecture fundamentals, covering core principles, design methods, step‑by‑step workflow, asset cataloging, conceptual and logical model creation, distribution blueprints, and cross‑domain theme modeling, ending with a concise summary.
This article introduces the design of a standalone notification‑system microservice, covering its functional requirements, conceptual and logical data models, and key workflow diagrams to guide implementation in Java‑based internet projects.
This article examines nine recurring data‑warehouse dilemmas, exploring when modeling is essential, how to evaluate model quality, the boundaries between data warehouses and business systems, the evolution of modern warehouses, career growth for data engineers, and the future role of data R&D in the AI era.
This article compares MySQL and Elasticsearch across data models, query languages, indexing, distributed architecture, performance, scalability, and typical use cases, helping developers choose the right system or combine them effectively for various application scenarios.
This article examines the evolution of data models—from conceptual, logical, and physical layers to hierarchical, network, relational, document, and graph structures—explaining their characteristics, implementation examples, and the contrasting schema‑on‑read versus schema‑on‑write approaches for modern data storage systems.
The article examines why front‑end product search fails during large marketing events, evaluates Elasticsearch's join feature and its drawbacks, compares nested, reverse‑modeling and flattened approaches, recommends reverse modeling for massive activity‑product data, and provides concrete DSL code, pagination and caching tips.
The article explores nine fundamental questions about data‑warehouse modeling—why and when to model, how to evaluate and compare models, the warehouse’s unique role versus business systems, modern architectural shifts, a quantitative value‑proof scoring framework, industry‑standard versus custom approaches, demonstrating business impact, and career insights—concluding that true value lies in enabling informed decisions rather than technology hype.
This document outlines a comprehensive data warehouse model design and review process, covering revision records, project overview, business requirements, conceptual and logical modeling, ETL workflow, exception handling, and acceptance criteria with practical examples and templates.
This article provides a comprehensive guide to designing the Data Warehouse Detail (DWD) layer, covering Kimball‑based design principles, step‑by‑step modeling, table and field naming conventions, concrete Hive DDL/DML examples, and optimization techniques such as partitioning, bucketing, and compression.
This case study details how the BIP procurement system tackled massive data volume, complex queries, and slow SQL by segmenting inbound orders, leveraging Elasticsearch, introducing a dynamic routing layer, and implementing robust ES high‑availability and monitoring, ultimately reducing query load by over 90%.
The article examines current limitations of AI agents in planning and complex reasoning, critiques existing methods like COT/TOT and ReAct, and proposes practical strategies—including combined COT‑Reflection approaches, structured memory algorithms, and white‑box interaction designs—to enhance agent performance within the DataFun knowledge map framework.
The article explains the three detailed-granularity fact table types—transaction, periodic snapshot, and cumulative snapshot—detailing their purposes, design principles, and comparative usage, and offers a simplified interpretation to help data engineers choose the appropriate fact table for data warehouse modeling.
This article offers a step‑by‑step guide for programmers to quickly onboard a new business or project by gathering documentation, mapping data models, understanding architecture, learning platform tools, and applying practical tips to accelerate learning and growth.
This article explains how major payment institutions ensure the accuracy of tens of millions of daily transactions and billions of dollars by using a four‑segment data model, three verification groups, error classification, and extensible data coding to achieve reliable settlement and accounting.
The article outlines Didi’s end‑to‑end metric lifecycle—from background, requirements and current pain points to a multi‑stage solution that introduces a unified metric dictionary, management tool, logical modeling, and consumption layer—to achieve accurate, timely, consistent, and efficiently managed indicators across the data warehouse ecosystem.
This article provides a comprehensive guide to using Scikit-Learn for data modeling, covering linear regression and decision tree algorithms, including data preparation, model training, evaluation metrics, and visualization techniques for predictive analysis.
This article introduces feed streams, explains their evolution from RSS to modern social feeds, classifies them by aggregation logic and display, discusses challenges such as real‑time performance and massive data, and presents a backend architecture with data models, pagination, write/read diffusion, and core publishing/reading workflows.
Facing inconsistent metrics and low reuse in siloed data services, the team built a unified metric service using a headless BI semantic layer and virtual data models, enabling consistent metric definitions, reusable data models, AI-friendly queries, and faster, scalable reporting across the organization.
This article introduces core relational database concepts, covering tables, data models and ER diagrams, the fundamentals of SQL, and the first three normal forms, providing clear explanations and examples to build a solid foundation for further database study.
This article presents Kuaishou's metric middle platform, detailing its background, design principles, architecture, metric management, data modeling, unified analysis language OAX, federated query engine OCTO, acceleration strategies, and future directions, illustrating how it improves data quality, development efficiency, and analytical capabilities at scale.
This article explains how data modeling drives business value by sparking conversations about data meaning, enabling the creation of useful data objects, and guiding smart decisions on data capture, storage, usage, and integration, while also outlining a governance framework for managing enterprise data models.
This article compiles comprehensive MySQL best‑practice guidelines covering charset selection, storage engine choice, naming conventions, field design, index optimization, SQL development tips, and operational procedures to improve performance, maintainability, and reliability of database systems.
This tutorial shows how to model nationwide train schedules as a graph, prepare CSV data, import it into Huawei Cloud GES, and write Cypher queries that discover faster, more flexible transfer routes than the official 12306 service, complete with Python code examples and visual results.
This article outlines Didi's approach to metric management, covering background, data product overview, and challenges in traditional and agile BI models, and presents a comprehensive solution for metric standardization, logical modeling, quality assurance, unified consumption, and future roadmap to improve data warehouse efficiency and consistency.
This guide outlines a practical six‑step methodology—covering overall planning, data integration, model construction, data development, asset management, and data services—to help enterprises build a robust data middle platform that unlocks business value and supports agile digital transformation.
This article walks through the fundamentals of data modeling, explaining what data is, the DIKW framework, why modeling matters, and detailing the end‑to‑end process from conceptual design through logical and physical layers, including DIM, DWD, DWS, and ADM tables with practical tips and naming conventions.
This article recounts L's attempt to change a train ticket, analyzes seat availability across four segments, and presents a minimal SQL model with table creation, data insertion, and a query that determines feasible seat combinations, demonstrating how segment‑wise seat allocation can enable complete journey booking.
Drawing from the book “Huawei Data Way”, this article explains why Huawei must digitize, describes the resulting data‑island problem, and outlines the four‑part framework of data governance—including data asset catalogs, standards, models, and distribution—while showing how business‑object‑centric information architecture is built and implemented.
This article presents a comprehensive overview of Ant Group's metric system, covering its definition, three-layer architecture, common challenges, concept consensus methods, semantic layer options, mechanism design, productization capabilities, platform improvements, business outcomes, future directions, and a detailed Q&A session.
This article reviews two decades of database research, analyzing the rise and decline of various data models—from hierarchical and relational to NoSQL, vector, and graph databases—while highlighting how AI, cloud, and hardware advances are reshaping DBMS architecture and predicting which approaches will dominate tomorrow’s data landscape.
This article presents Kuaishou's metric middle platform, detailing its background, design principles, metric management and service architecture, including headless BI concepts, unified analysis language OAX, query engine OCTO, data modeling layers, acceleration strategies, and future directions toward intelligence and high performance.
Data architecture governance, encompassing standards, security, modeling, quality, and lifecycle management, is essential for digital transformation in fast‑growing industries like express delivery, and this article outlines current challenges, traditional approaches, and a practical, phased methodology with platform support to implement effective governance.
This article presents Ant Group's data‑driven approach to digital transformation and risk control in the bulk commodity sector, covering background challenges, data‑application pain points, core capabilities, credit‑risk models, data‑asset construction, indicator frameworks, and secure data integration for B2B scenarios.
This article explains how companies can integrate independent user‑profile tag systems into a unified framework, covering tag definitions, demand sources, classification, construction methods, update cycles, platform architecture, common algorithms, and practical applications such as marketing, KPI attribution, and A/B test analysis.
This article outlines the step‑by‑step evolution of Baidu's Feed data warehouse—from traditional layered modeling to hour‑level core tables, then real‑time wide tables, and finally a flow‑batch integrated multi‑version wide‑table architecture—highlighting the motivations, design choices, challenges, and resulting benefits.
Baidu’s Mobile Ecology team transformed its Feed data warehouse through three progressive stages—hour‑level core tables, a real‑time wide table, and a unified day‑level multi‑version table—consolidating traffic, content, and user data into a single partitioned wide‑table architecture that resolves granularity inconsistencies, cuts processing cost, and delivers real‑time to daily latency for diverse analytics.
This article explains the concept of data standards, outlines the three main categories of data standards, describes a four‑stage implementation process, and provides a real‑world bank case study to illustrate how enterprises can establish and apply data standards for better data quality and value.
This presentation by senior data warehouse engineer Huang Luo details Wing Payment’s end‑to‑end data warehouse build, covering background challenges, governance framework, platform architecture, layered modeling, naming standards, asset management, monitoring, and future plans, illustrating how systematic data governance drives cost reduction, efficiency, and security.
This article details JD Retail’s end‑to‑end data platform, covering data asset certification, 5W2H modeling, unified query DSL, intelligent acceleration, robust governance, visualization components, low‑code orchestration, and large‑model AI applications that together reduce query latency, cut development costs, and empower analysts across the retail business.
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.
This article details how the Dongchedi business team designs, implements, and monitors a comprehensive metric system within its data warehouse, covering metric standards, model construction, metadata management, quality monitoring, application scenarios, and future directions using the DataLeap platform.
