This article shares Ant Group's extensive experience in big data governance, detailing the overall data governance framework, data quality management, data storage governance, and future considerations, illustrated with practical cases and strategies for ensuring compliance, reliability, and cost efficiency.
This article outlines Didi’s best‑practice approach to constructing a global ride‑hailing data metric system, covering business scenarios, metric categories, pain points such as definition and technical challenges, and a comprehensive solution involving organizational structure, processes, model design, tooling, timezone handling, and governance.
DingoDB is a multi‑modal vector database that unifies storage and analysis of structured, semi‑structured and unstructured data through a Raft‑based distributed architecture, offering MySQL‑compatible SQL, high‑performance APIs, automatic sharding, real‑time index optimization, and hybrid scalar‑vector queries for enterprise knowledge bases, LLM memory, and real‑time decision‑making.
This presentation by senior data warehouse engineer Huang Luo details Yipay's end‑to‑end data warehouse build, covering background challenges, governance framework, platform development, layered architecture, naming standards, monitoring, and future plans, offering practical insights for data engineers, architects, and business stakeholders.
Vivo’s trillion‑scale data platform evolved into a dual‑active real‑time and offline architecture that leverages multi‑datacenter clusters, Kafka/Pulsar caching, a unified sorting layer, HBase‑backed dimension tables, and micro‑batch Spark jobs to deliver low‑cost, high‑performance processing, 99.9% availability, and 99.9995% data‑integrity.
This article outlines the evolution of software architecture styles, describing various architectural domains and sub‑domains—from web and mobile applications to integration, data, and analytics architectures—and their typical implementations, illustrated with a detailed classification table.
This article details how CaoCao Mobility transformed its ride‑hailing platform by replacing a traditional Lambda architecture with an enterprise‑grade real‑time data warehouse built on Hologres and Flink, covering business motivations, architectural design, component capabilities, performance optimizations, operational safeguards, and future roadmap.
Designing a data governance system involves defining data architecture, standards, and master data, documenting mechanisms, aligning them with actual business and analytical data processes, forming joint teams for specialized governance activities, and institutionalizing these practices through formal processes to sustain continuous data capability across the enterprise.
This article explains what a data lake is, its origins, key characteristics such as collecting all data, enabling diverse user access, and flexible processing, compares it with traditional data warehouses, discusses cost advantages, potential pitfalls like data swamps, and outlines best‑practice considerations for enterprise adoption.
The article explains why unified data architecture is essential to avoid duplication and inefficiency, discusses differing performance trade‑offs among batch, streaming, and interactive analytics, introduces an incremental computation model that unifies these modes, and invites readers to a Dec 19, 2023 technical sharing event.
Data lakes and data warehouses serve different purposes in big‑data architectures; this article explains their definitions, core attributes, five major distinctions—including data retention, type support, user coverage, adaptability, and insight speed—and offers guidance on selecting or combining the two approaches.
This article explains how Kuaishou's e‑commerce team built a unified OLAP‑based data platform—covering data ingestion, consistent dimensional and fact layers, metric management, and real‑time services—to address rapid growth, metric inconsistency, and operational inefficiencies across multiple business scenarios.
This article explains why Kuaishou built a data lake, outlines the shortcomings of its previous Lambda architecture, describes the adoption of Apache Hudi for unified batch‑stream processing, and details the five major technical challenges and the corresponding solutions implemented to improve performance, consistency, and operational reliability.
This article explains the data mesh architecture—its domain‑driven, decentralized design, key concepts, benefits over traditional data lakes, a scoring method to evaluate suitability, and the importance of observability for trustworthy data products.
The article analyzes how enterprise data systems must be re‑engineered for large‑model applications, outlines the three‑stage data pipeline (ingestion, orchestration, interaction), introduces data‑virtualization techniques with virtual tables and intelligent materialization, and validates the approach with two banking case studies.
This article provides a comprehensive overview of GPFS (IBM Spectrum Scale) and Ceph, detailing their architectures, core components, deployment models, use cases, performance considerations, and a comparative analysis with GlusterFS, while also discussing future trends in distributed storage.
Manbang Group shares its practical experiences and insights on real-time computing, multi‑cloud platform architecture, data warehousing with Flink and Holo, real‑time decision and feature platforms, and future plans for scaling these systems to support logistics and recommendation algorithms.
This article traces the evolution of data architecture from early BI systems through traditional big‑data stacks, streaming, Lambda and Kappa designs, and explains how a unified stream‑batch model simplifies development while keeping logic consistent across data‑analysis and pipeline applications.
This article examines the middle‑platform concept in large companies, especially data departments, outlining its benefits such as resource centralisation, reduced duplication and experience sharing, while also highlighting drawbacks like slow response, excessive authority, and the need to bring platform capabilities closer to business units.
The panel discusses the three‑stage evolution of data platforms, compares US and Chinese market dynamics, evaluates Snowflake’s success factors, and outlines the criteria and opportunities for a China‑specific Snowflake‑like solution, while also sharing investment perspectives on data‑driven startups.
This article explores the evolving concept of data lakes, compares them with traditional data warehouses across storage, modeling, tooling, and user roles, and examines the emerging lake‑warehouse integration, highlighting why both remain essential in modern big‑data architectures.
The article explains the concept, benefits, and detailed modular architecture of a data middle platform, covering data storage, acquisition, processing, governance, security, and operation frameworks, and illustrates how enterprises can build and evolve such platforms to turn data into valuable services.
This article explains data architecture and data modeling fundamentals, presents DAMA DMBOK concepts, outlines four practical solutions for model design, standard management, automated change control, and business mapping, and shares an enterprise manufacturing case study with Q&A on governance and efficiency.
This article explains why channel data products are needed, outlines the stages of growth business, describes how to design and implement a channel data product—including its architecture, database schema, and operational workflow—and concludes with a practical summary and Q&A.
This technical presentation reviews the historical development of big data platforms, outlines the four generations of cloud data platform architectures, details the modern cloud‑native stack—including unified metadata, scheduling, and integration systems—and showcases a real‑world industrial manufacturing case with a Q&A session.
The article explains how enterprises can build a comprehensive big data analytics platform—covering data collection, storage, computation, and decision layers—by clarifying business scenarios, choosing appropriate on‑premise or cloud deployment, selecting suitable architectures such as Lambda/Kappa, and addressing component choices and emerging technical trends.
This article provides an in‑depth overview of enterprise data architecture, covering data architecture components, business drivers, modeling techniques, measurement metrics, key concepts, tools, and best practices for designing, implementing, and maintaining robust data models and databases.
This article details the evolution of Tencent Music's content data platform from version 1.0 to 4.0, describing the migration from ClickHouse to Apache Doris, the introduction of a semantic layer, optimization of data ingestion, query performance, and cost reduction strategies that dramatically improved data timeliness, operational efficiency, and storage costs.
This article explains data governance by presenting authoritative definitions, outlining a five‑level requirement hierarchy inspired by Maslow, and detailing practical considerations for stability, security, usability, quality, and cost‑value to help organizations implement effective data governance frameworks.
This article details how ZuanZuan leveraged ClickHouse as the core OLAP engine for its massive self‑service analytics platform, covering OLAP engine selection criteria, system architecture, real‑world use cases, performance tuning, operational challenges, and future development plans.
iQIYI’s data lake combines public‑cloud and private storage with Apache Iceberg’s snapshot‑based table format to enable near‑real‑time, unified batch‑and‑stream analytics, reducing costs, simplifying architecture, and improving data freshness across use cases such as log collection, audit, pingback, and member order processing.
This article details how Tongcheng Data Science built a real‑time analytical data warehouse using Apache Doris, covering business scenarios, the evolution from a legacy 1.0 architecture to a Doris‑based 2.0 design, deployment topology, development workflow, operational benefits, and future roadmap.
Facing massive daily data volumes and complex, ad‑hoc analytical needs, Zhaozhuan’s engineering team evaluated multiple OLAP engines and chose ClickHouse, then built a four‑layer self‑service analytics platform, detailing architecture, use‑cases, performance tuning, large‑scale joins, and future roadmap challenges.
This article examines why ClickHouse was chosen as the OLAP engine for a massive self‑service analytics platform, describes the system architecture, shares concrete memory and performance tuning parameters, and outlines current challenges and future roadmap for large‑scale real‑time data analysis.
This article examines the selection of ClickHouse as the OLAP engine for a self‑service analytics platform, describes the platform’s architecture, details memory and performance tuning techniques, discusses large‑scale join handling, and outlines current challenges and future development directions for ClickHouse.
The article explains how to formally name data schemas, structures, and models, clarifies the differences between internal, external, logical, and physical schemas, and proposes a systematic hierarchy—including strategic and tactical schemas—to improve data architecture and reduce confusion in enterprise data design.
This article explains what data architecture is, why it is essential for linking business and technology, outlines its main components such as data models, data flows, value streams and standards, and discusses emerging trends toward service‑oriented, consumption‑focused data architectures.
This article details Baicaowei’s journey from a Hadoop‑based data platform to a modern StarRocks‑driven architecture, illustrating how digitalization, evolving business needs, and streamlined data pipelines empower the fast‑moving consumer goods sector through efficient data collection, modeling, and analytics.
This article traces the history of data lakes from their 2010 inception with Hadoop through cloud‑native object storage, lakehouse formats like Delta Lake, and Alibaba Cloud's multi‑layer solution, outlining key architectural stages and practical construction challenges for enterprise‑grade implementations.
The TDengine Developer Conference in Beijing unveiled the open‑source, cloud‑native TDengine 3.0, detailing its revolutionary architecture that tackles high‑cardinality challenges, introduces RAFT‑based distribution, and showcases real‑world IoT and IT‑operations case studies where enterprises dramatically improved performance and reduced costs.
This article explores the evolution and architecture of cloud‑native data platforms, covering their historical roots, modern components such as storage layers, ingestion, processing, metadata, and consumption, and offers practical guidance on selecting tools, designing pipelines, and implementing best‑practice strategies for scalable, flexible data infrastructure.
The article compares Data Fabric and Data Mesh as modern data‑architecture approaches, explains their technical and organizational differences, discusses the ongoing debate between data lakes, warehouses, and lakehouses, and highlights how each option fits varying data‑type and usage scenarios.
In this interview, Tencent Cloud expert Zhang Zhigang explains the fundamentals and key technologies of lakehouse architecture, discusses how cloud‑native practices enhance its performance and operability, and offers practical advice for big‑data professionals ahead of the 2022 GIAC Global Internet Architecture Conference in Shenzhen.
The article traces the evolution of data platforms from early data warehouses—using schema‑on‑write, columnar storage, and MPP engines—to data lakes that retain raw data with schema‑on‑read, and finally to lakehouse architectures that merge storage and compute, offering unified metadata, versioning, and support for BI, big‑data, AI, and HPC workloads.
This article explains data architecture as the transformation of business needs into data and system requirements, outlines its objectives, core principles, essential components, the relationship with data modeling, relevant frameworks, and modern best‑practice guidelines for building scalable, cloud‑native, AI‑enabled architectures.
This article chronicles JD Baitiao’s journey from a monolithic MySQL setup through NoSQL and DBRep to a mature ShardingSphere‑based sharding solution, highlighting the technical motivations, architectural decoupling strategies, evaluation criteria, performance comparison, and the operational benefits achieved for a high‑traffic financial service.
This article examines JD Baitiao’s journey from monolithic MySQL to NoSQL and DBRep, detailing how the team evaluated and ultimately adopted Apache ShardingSphere for scalable, low‑coupling database sharding, highlighting performance, maintenance, and business-driven architectural decisions.
This article presents a comprehensive technical walkthrough of Sensor Data's multi‑dimensional analysis platform, covering product architecture, an Impala‑based real‑time query engine, query performance tuning, resource‑estimation strategies, and future plans, with concrete diagrams, test results, and community contributions.
This article details iQIYI's design and implementation of a unified OLTP data center that consolidates data across business lines, solves data‑island issues, ensures strong consistency between MongoDB and Elasticsearch, and provides high‑availability, massive‑scale storage for billions of records.
This article explains what a data middle platform is, why organizations need it, its core principles, technical architecture, and practical implementation guidelines, highlighting how it solves issues like inconsistent metrics, duplicate data construction, low query efficiency, poor data quality, and high development costs.
This article explains the concepts of data warehouses, data marts, and data lakes, illustrates why the lakehouse model emerged to bridge storage and compute, and outlines its key benefits such as flexibility, scalability, reduced redundancy, and unified analytics for modern enterprises.
This article presents MobTech's comprehensive data governance and security practices, covering the necessity of governance, challenges in large‑scale data environments, the full‑link governance chain, modular architecture, and specific implementations for financial risk‑control scenarios.
This article explains how to build a unified stream‑batch real‑time data warehouse using FlinkSQL, covering prerequisite knowledge, five core concepts, two implementation approaches, a comparison of traditional versus real‑time architectures, and a comprehensive hands‑on example, illustrated with diagrams.
This article explains microservice fundamentals, outlines their advantages and technical traits, and provides practical guidance on data design, database selection, polyglot persistence, scaling strategies, and why MongoDB often fits microservice architectures best.
This article explains data governance fundamentals, why it is needed, its objectives, core components, implementation methodology, required tools, and real‑world practices from Meituan and Ant Financial, providing a comprehensive guide for managing data as a strategic enterprise asset.
This comprehensive article explains what a data lake is, its core characteristics, reference architecture, major cloud vendor implementations, typical use cases such as advertising and gaming, step‑by‑step construction guidance, and future trends in cloud‑native big‑data platforms.
This article presents a comprehensive overview of master data management concepts, maturity levels, and the challenges faced by Baidu smart mini‑programs, followed by a detailed practical architecture design—including domain modeling, high‑availability microservice implementation, performance optimization, and data synchronization—while also discussing future extensions and team capability building.
The article explains what a data lake is, its architecture and core capabilities, compares it with data warehouses, discusses its value and challenges, and reviews major open‑source platforms such as Delta Lake, Iceberg, and Hudi.
Facing rapid business iteration, Baidu’s iFanFan data team designed a unified real‑time and offline big‑data platform, tackling business, technical, and organizational challenges through Lambda/Kappa architectures, data integration, storage, computation, governance, and scalable analytics to deliver timely, accurate, and valuable data products.
This article, discovered on the Yunqi community site, provides a system‑architecture perspective overview of current big‑data architecture hotspots, development trajectories, emerging trends, and unresolved challenges, while highlighting the field’s rapid evolution and recommending a curated list of in‑depth resources for further study.
This article breaks down a typical big data platform architecture into its four layers—data collection, storage and analysis, sharing, and real‑time computation—detailing the essential tools such as Flume, HDFS, Hive, Spark, DataX, and task scheduling systems that enable scalable, low‑latency data processing and delivery.
This article explains the typical architecture of a big‑data platform, detailing its four core layers—data collection, storage & analysis, data sharing, and application—and describing the key technologies such as Flume, DataX, HDFS, Hive, Spark, Spark Streaming, and task scheduling components.
The article outlines a typical big data platform architecture, detailing its core layers—data collection, storage and analysis, sharing, application, real-time computation, and task scheduling—while describing key technologies such as Flume, DataX, HDFS, Hive, Spark, Spark Streaming, and Redis.
This article traces two decades of application data architecture evolution, comparing traditional single‑system LAMP designs with modern multi‑component cloud‑native stacks, and offers practical guidance on scaling, component selection, CDC‑based data derivation, and cloud‑native implementations such as Tablestore.
The article explains the concept of a data middle platform, describing its architecture, the essential big‑data foundation, metadata management, data service components such as BI and tag systems, and how these layers together enable unified data access, governance, and business intelligence across enterprises.
This article explains how object storage emerged as a cloud-native solution that surpasses traditional DAS, SAN, and NAS architectures by offering virtually unlimited capacity, robust metadata handling, and simple RESTful APIs for modern applications and large‑scale data workloads.
This article explains the concepts of file, object, and block storage, compares their architectures and access methods, and outlines practical scenarios where each storage type best fits, helping you choose the optimal solution for your workload.
This article presents the technical solution and key processes of JD's mini‑program custom data analysis service, covering business background, ClickHouse‑based storage design, real‑time processing pipelines, dynamic rule parsing, table architecture, monitoring mechanisms, and future outlook for large‑scale data analytics.
This article explains QingStor's object storage concepts, core advantages, global data model, subsystem design, massive small‑file optimizations, key features like lifecycle management and cross‑region replication, and showcases a traffic‑industry use case, highlighting its scalability, reliability, and ease of integration.
Airbnb’s 2019 Data Quality Improvement Plan reorganized its data‑engineering workforce, introduced a dedicated data‑engineer role, adopted a decentralized Minerva‑based architecture with Spark pipelines, instituted rigorous testing, governance, and certification processes, and established SLAs and monitoring to ensure timely, trustworthy, well‑documented data across the enterprise.
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.
This article explains what tags are, how they function as data assets, defines the concept and architecture of a data middle platform, and demonstrates why tags are the pivotal element that enables enterprises to turn raw data into valuable, reusable business services.
This article explains why data warehouses are critical for decision‑making, outlines the challenges of immature warehouses, and provides a step‑by‑step framework—including goal setting, technology selection, problem identification, domain modeling, layer design, modeling principles, and governance standards—to help teams build a robust, maintainable data warehouse.
This article presents a comprehensive guide to data model architecture, detailing layered data store definitions, classification structures, processing flow, naming conventions, and core design principles to help engineers build scalable, maintainable data warehouses.
This article outlines a comprehensive data model architecture, detailing layered data stores, classification structures, naming conventions, and core modeling principles to guide effective data warehouse design and implementation.
This article details the billing group's challenges with single‑node MySQL, the evaluation of alternative databases such as TiDB and CockroachDB, performance testing, migration strategies, operational best practices, and the final decision to adopt TiDB for scalable, high‑availability data services.
The article reviews Anthony J. Algmin’s reflections on past data‑architecture predictions, current hot topics such as cloud, AI/ML, data governance, and real‑time analytics, and forecasts future trends including metadata management, blockchain, and the evolving role of data architects within enterprises.
This article outlines Meituan's journey in data governance, detailing the challenges of data quality, cost, security, standardization and efficiency, and presenting a three‑stage roadmap—passive, proactive, and automated governance—along with concrete technical and organizational solutions.
This article explains the concept of a data lake—its origin in 2011, how it differs from traditional databases and data warehouses, its core characteristics such as raw data storage, on‑demand computing, and schema‑on‑read, as well as its advantages, challenges, architectural components, and future outlook within the big‑data ecosystem.
Data warehouses store structured data centrally for reporting and analysis, while data lakes retain raw data in various formats, offering flexible, low‑cost, schema‑on‑read processing; the article explains their definitions, key differences, common misconceptions, and why many organizations now combine both to enable self‑service big‑data analytics.
This article explains the fundamental distinctions between data lakes and data warehouses, outlines five critical differences—including data retention, type support, user support, adaptability, and insight speed—and offers guidance on selecting the appropriate solution based on organizational needs and technology options.
This article clarifies the relationships among Business Architecture (BA), Data Architecture (DA), Application Architecture (AA) and Technology Architecture (TA), explains their roles within strategic, business, and solution layers, and walks through a concrete stock‑purchase example to illustrate end‑to‑end design and implementation.
This article explains what a data lake is, outlines its key characteristics, and compares three major data lake frameworks—Iceberg, Hudi, and Delta Lake—highlighting their architectures, features, and trade‑offs for large‑scale data storage and processing.
This article examines CMB’s award‑winning real‑time business‑flow monitoring and operations platform, detailing how map‑and‑navigation‑based architecture transforms banking IT, enhances customer experience, enables fine‑grained intelligent operations, and outlines a future driven by big‑data analytics.
This article explains the importance of data‑warehouse modeling, outlines a layered architecture (DWD, DWS, DIM, ADS), describes a systematic modeling process, and presents design principles and practical examples to achieve high‑quality, stable, and efficient data models for large‑scale analytics.
This article provides an extensive overview of data warehouse and data lake concepts, their architectures, differences, components, and implementation considerations, covering topics such as OLTP/OLAP, ETL processes, data quality, cloud solutions, and the role of data platforms in modern enterprises.
This article provides a comprehensive overview of data warehouses, data lakes, and data middle platforms, explaining their definitions, architectures, functions, differences, and the value they bring to enterprises, while also addressing common misconceptions and related concepts such as data marts and data swamps.
The 10th PostgreSQL China Technical Conference in January 2021 featured expert talks on scaling Citus clusters with logical replication and on applying PostgreSQL to Suning’s retail digitalization, highlighting architecture, challenges, technology choices, and future directions for database-driven business transformation.
This article examines the 20‑year evolution of big data architectures, contrasts data lakes and data warehouses, explores their respective strengths and challenges, and details Alibaba Cloud’s lake‑warehouse (lakehouse) solution that unifies storage, metadata, and compute for enterprise‑grade analytics and AI workloads.
This article details how Ctrip's senior engineering manager leveraged ClickHouse to build a high‑availability, sub‑second response data platform handling nearly 700 billion rows, describing the motivations, architecture, data synchronization processes, performance gains, challenges, and practical recommendations for large‑scale analytics.
This article examines how leading Silicon Valley companies such as EA, Twitter, Airbnb, and Uber design and operate data middle platforms—detailing their architectures, data collection pipelines, standardization efforts, real‑time and batch processing, and the business impact of shared data capabilities.
This article details the three‑stage evolution of 58.com’s commercial data warehouse, describing its massive scale, four‑layer architecture, technical challenges, migrations from MapReduce to Hive and Flink, real‑time streaming upgrades, and the resulting improvements in stability, accuracy, and timeliness.
This comprehensive article explains what a data lake is, outlines its core characteristics and reference architecture, compares major cloud providers' data‑lake offerings, presents typical advertising and gaming use cases, and proposes a practical, agile process for building and operating a data lake.
This article explains the fundamentals of object storage, compares it with block and file storage, outlines its architecture, components, advantages, use cases, and limitations, showing why it has become the dominant storage model in modern cloud environments.
This article presents a comprehensive guide to constructing real-time data warehouses using Apache Flink, covering the motivations, design principles, application scenarios, layer-by-layer architecture, metadata and lineage management, quality assurance, and the supporting toolchain for reliable streaming analytics.
This article explains Elasticsearch’s cluster and data layer architecture, detailing nodes, indices, shards, replicas, deployment models, and the trade‑offs of different distributed system designs, helping readers understand how Elasticsearch achieves scalability, reliability, and query performance.
This article compares data warehouse, data lake, data platform, and data middle platform, explaining their definitions, architectures, strengths, limitations, and use‑case differences, and provides tables that highlight how each solution handles structured and unstructured data, governance, flexibility, and business value.
The article explains what a data middle platform is, why it differs from a traditional big‑data platform, the efficiency, collaboration and talent challenges it addresses, its definition as a data‑driven innovation layer built on big data, cloud and AI, and outlines its logical architecture centered on data APIs.
This comprehensive guide outlines the entire big data technology ecosystem, covering foundational techniques, data acquisition, transmission, storage, processing frameworks, governance, and the most popular tools and platforms that power modern large‑scale data solutions.
This article provides an in‑depth, English‑language overview of data lakes, covering their definition, core characteristics, reference architectures, major cloud‑vendor implementations (AWS, Huawei, Alibaba Cloud, Azure), typical industry applications such as advertising and gaming, as well as practical guidance on building and evolving a data lake in a cloud‑native, big‑data environment.
