This article outlines Alibaba Cloud’s ODPS evolution, its multi‑engine architecture, recent MaxCompute and Hologres enhancements, and how these innovations deliver scalable, fast, and simple big‑data solutions for diverse cloud‑native scenarios.
This article details Kuaishou’s evolution of its metadata management platform—from early Hive‑centric beginnings to a unified 2.0 architecture and a forward‑looking 3.0 vision—highlighting challenges, key technologies, and how metadata drives data production, consumption, governance, and cost optimization across the big‑data middle platform.
This guide outlines the essential concepts of big data, the roles of a front‑end data team, practical workflow steps, platform architecture, industry benchmarks, and actionable strategies for small teams to improve efficiency, visualization capabilities, and digital operations.
The talk outlines the evolution of Alibaba Cloud’s integrated big data and AI platform, highlighting the three‑V fundamentals, the AI‑inspired usability‑scale‑efficiency triangle, open‑source trends, and how the platform unifies offline and real‑time analytics while simplifying governance and development.
This article describes how Meituan Food Service SaaS built a high‑quality, large‑scale data middle platform using StarRocks, covering business overview, technical selection, multi‑layer architecture, virtual views, intelligent tiered querying, multi‑active hot standby, and the performance gains achieved.
Tencent's Oula platform, launched in 2019, provides a DataOps‑driven, end‑to‑end data governance solution covering data discovery, asset factory, metric platform, and governance engine, and the talk details its construction goals, data development governance, unified metric system, data map, and Q&A on asset health and lineage.
This article details how Xiaomi integrated the open‑source Kyuubi SQL gateway into its evolving big‑data platform, describing the challenges of multiple SQL services, the architectural redesign for a unified, high‑availability service, performance gains, new features such as engine pooling and Z‑ordering, and future roadmap plans.
This article explains how X‑Select’s Data Quality Platform (DQC) addresses common data quality problems in large‑scale data development by defining six quality dimensions, leveraging open‑source solutions such as Apache Griffin and Qualitis, and implementing rule definition, execution, alerting, and workflow interruption within a Spark‑based architecture.
This article presents a comprehensive overview of Zhihu's Data Management Platform (DMP), covering its business background, three core business modes, detailed architecture, offline and real‑time data pipelines, feature storage design, performance optimization techniques, and future iteration directions.
The DataFun Summit 2022, held on September 17, gathered leading experts from Baiji Whale Open Source, NetEase, Tapdata, and Alibaba Cloud to share deep technical insights on SeaTunnel V2 architecture, DataOps implementations, and open‑source big‑data studio tools, offering attendees practical guidance for modern data platforms.
This article details ZhongAn Insurance's digital transformation through its 4633 data‑centric framework, the architecture of its JiZhi data platform, the challenges of its original ClickHouse‑based real‑time warehouse, and how migrating to StarRocks improved performance, scalability, and operational efficiency across advertising and insurance use cases.
The article introduces Tencent's big‑data platform philosophy and overall architecture, detailing three generations of evolution from offline Hadoop‑based processing to real‑time Spark/Storm integration and finally AI‑driven machine‑learning platforms, while also highlighting the team, book publication, and a related giveaway event.
JD’s article details its big‑data platform’s cross‑domain and hierarchical storage solutions, describing the challenges of multi‑datacenter data synchronization, the architecture of its storage layer, the implemented asynchronous and synchronous data flows, topology management, metadata tagging, and performance‑enhancing techniques for efficient, disaster‑resilient data handling.
This article presents Huya's big data platform evolution, detailing cost‑saving measures, SLA practices, multi‑datacenter architecture, containerized resources, metadata‑driven intelligence, and future directions such as hybrid‑engine materialized views to improve efficiency and service reliability.
The article details Meituan's massive Kafka deployment—over 15,000 machines handling more than 30 PB of daily data—its performance and management challenges, and the comprehensive application‑layer, system‑layer, and hybrid‑layer optimizations Meituan implemented to reduce read/write latency and improve large‑scale cluster reliability.
This talk presents the end‑to‑end deployment of Kyuubi as a unified, high‑availability SQL gateway on Xiaomi’s big‑data platform, covering its integration, architecture upgrades, multi‑engine support, performance gains, operational improvements, and future roadmap.
The article describes how Ctrip built a unified data service platform that standardizes API development, leverages multiple storage engines, introduces token‑based security, Sentinel rate‑limiting, caching, and automatic contract generation to dramatically cut development cycles and improve reliability for big‑data workloads.
The article presents an Indicator Testing Platform that automates metric validation—covering timeliness, completeness, accuracy, and consistency—through model‑level comparison, regression, online monitoring, and TDD‑style testing, dramatically reducing manual effort and enabling rapid detection and correction of data quality issues across thousands of business indicators.
The article examines why traditional centralized data lake architectures fail at scale, outlines common failure patterns, and proposes a paradigm shift toward a distributed data mesh that treats domain‑oriented data as a product, leverages self‑service platforms, and aligns engineering, product, and governance practices for modern enterprise data platforms.
This article describes how the gaming company Boke City rebuilt its comprehensive data service platform by replacing a CDH‑based Impala solution with StarRocks, detailing the architectural changes, performance benchmark results, and the resulting improvements in query speed, real‑time data updates, and operational simplicity.
This article explores the concept of architecture autonomous services as a self‑service data platform for architecture governance, outlines a four‑stage roadmap, provides concrete Log4j tracking examples using SourceGraph and ArchGuard, and details practical steps to build a data foundation, abstract service interfaces, integrate BI, and design metric‑driven evolution.
This article details Qunar's multi‑year journey of designing and evolving a full‑stack BI platform—covering data ingestion, storage, query engines, self‑service analytics, and real‑time OLAP—by iterating through three development phases, selecting technologies such as Impala, Kudu, ClickHouse and Apache Druid, and addressing performance, usability and governance challenges to empower business users with fast, reliable data insights.
The article details JD International's challenges and solutions in building a unified, real‑time data platform for its multi‑regional 618 promotion, covering business characteristics, data distribution, team organization, dashboard architecture, integration strategies, and short‑ and long‑term technical plans.
The article explains what a data middle platform is, why enterprises need it for big‑data‑driven decision making, AI, and operational efficiency, and outlines four key reasons and practical principles for building and evolving such a platform within modern digital organizations.
This article presents JD's large‑scale big‑data platform, detailing its overall architecture, the challenges of cross‑region storage, the design of a unified cross‑domain data synchronization mechanism, and the implementation of tiered storage to improve performance, cost efficiency, and data reliability across multi‑datacenter clusters.
This article presents OPPO's commercial data system construction practice, covering the data platform strategy, ingestion pipelines, development efficiency toolkits, data validation, visualization aids, UDF principles, warehouse architecture, metric systems, dimensional modeling, ETL optimization, governance metadata, quality management, monitoring, attribution services, analytics reporting, and a Q&A session.
Bilibili’s data platform consolidates scattered metadata into a unified URN‑based model stored across TiDB, Elasticsearch, and HugeGraph, offering batch‑pull and embedded collection, flexible SQL‑like queries, comprehensive lineage mapping, and powering data‑map, lineage‑map, and impact‑analysis tools while planning expanded quality assurance and self‑service dictionaries.
This article explains the main layers of a typical big data platform—data collection, storage and analysis, sharing, and application—detailing common tools such as Flume, DataX, Hive, Spark, SparkSQL, Impala, and Spark Streaming, and discusses task scheduling and monitoring in the ecosystem.
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.
This article describes how Halodoc’s data engineering team identified limitations of their Redshift‑based platform, evaluated a LakeHouse design, selected Apache Hudi for mutable data handling, and outlined the challenges and benefits of building a scalable, decoupled storage‑compute architecture for their growing healthcare services.
This article explains the background and importance of data governance, defines data quality and its key metrics, describes the monitoring and alerting modules of the IData data platform, outlines built‑in and custom rule templates, and discusses baseline management and future enhancements.
Zhamak Dehghani argues that enterprise data architects should replace centralized data warehouses and lakes with a distributed data mesh that leverages domain‑owned data products, addressing scalability, coupling, and ownership failures through a paradigm shift toward self‑serve, discoverable, and interoperable data components.
This article details the architecture of Sensors Data's analytics platform, the implementation of a real‑time Impala query engine, multiple query‑performance optimizations—including storage redesign, user‑behavior sequence tuning, join elimination and expression push‑down—and a resource‑estimation framework that dramatically reduces query failures and latency.
Meituan’s data platform runs Kafka on over 7,500 machines, handling daily traffic exceeding 21 PB, and tackles latency, slow nodes, and massive cluster management through layered optimizations—including disk balancing, pipeline acceleration, fetcher isolation, cgroup isolation, SSD caching, isolation strategies, full‑link monitoring, lifecycle management, and TOR disaster recovery.
This article explains the concept, necessity, principles, and practical implementation of a data middle platform, outlining its methodology, organizational structure, technical architecture, and the challenges it solves such as inconsistent metrics, data duplication, low query efficiency, poor quality, and high construction costs.
The article details Kwai’s OneService platform—a low‑code, self‑service data platform that streamlines API creation, deployment, and operation, covering its background, architecture, key technologies such as API matrix and configuration‑as‑code, high‑availability and performance strategies, achieved results, and future roadmap.
This article presents an in‑depth overview of Youzan's data platform, introduces the DP data development platform with its key features and workflow, details the core module architecture—including service, scheduling, and component layers—and explains the migration from Airflow to DolphinScheduler to improve performance, stability, and scalability.
This article details how NetEase's DataFunTalk presentation explores the growing data quality challenges in data development and demonstrates the application of DataOps principles—including pre‑ and post‑control mechanisms, sandbox environments, and automated governance tools—to systematically reduce defects, optimize resources, and ensure reliable data delivery across the company's diverse business lines.
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 details how Ctrip’s hotel tech team built an information‑graph platform using Nebula Graph to link user scenarios with hotel data, addressing challenges of scene‑information mapping, data richness, and operational efficiency through a unified backend architecture, deployment strategies, and schema design.
The article details Vipshop's multi‑dimensional use of SeaTunnel to integrate Hive and ClickHouse, describing data import/export challenges, tool selection among DataX, SeaTunnel and Spark, custom configurations, platform integration, and future improvements for high‑performance OLAP pipelines.
This article traces the evolution from traditional data warehouses to modern data lakes and data middle platforms, explains why siloed data development hampers efficiency, and details the architecture and indicator‑library design used by Zhengcaiyun to achieve unified, reusable data services.
This article presents a comprehensive overview of constructing an intelligent risk control tool system, detailing its evolution from manual processes to automated platforms, describing the core "three‑piece" suite (model, decision, and feature platforms) along with supporting data and monitoring platforms, and explaining the functions and interactions of each module such as data ingestion, feature engineering, automated modeling, decision flow, and real‑time monitoring.
ByteDance’s data platform, built over seven years, now handles exabyte-scale data and over 100 million TPS, using a hybrid “middle‑platform + Business Partner” model, custom engines like ClickHouse/ByteHouse, agile governance, and a suite of products to support internal and external businesses, illustrating large-scale big-data engineering practices.
This article shares Beike Zhaofang’s two‑year experience building a data governance center, covering the purpose and scope of governance, how the company tailors the focus to its business and system characteristics, the middle‑platform construction approach, project goal management, product and operation rollout, and the challenges and solutions encountered.
The article examines DP's rapid growth in daily scheduled tasks, outlines the limitations of its Airflow‑based scheduler, compares Airflow with DolphinScheduler, and details the architectural redesign, migration steps, and future plans for a more scalable, reliable big‑data workflow system.
This article outlines Beike Zhaofang's data governance framework, covering its purpose, three core dimensions—sharing, accuracy, and usability—how the scope is focused based on company characteristics, the necessity of data convergence and efficiency building, product architecture, target management, operational experiences, and challenges with corresponding measures.
iQIYI’s Basic Data Platform unifies data exchange across dozens of business lines by providing massive storage, distribution, online query and offline analysis services, employing an access layer, unified management, fine‑grained governance, dual‑cluster ID generation, active‑standby HBase with MongoDB WAL, RocketMQ messaging with server‑side filtering, and horizontally scalable read replicas to ensure high availability and performance.
This article explains the SnQL query language used by Sentry’s Snuba data platform, covering its query structure, JSON request format, and detailed syntax for MATCH, SELECT BY, WHERE, HAVING, ORDER BY, LIMIT, GRANULARITY, TOTALS, and SAMPLE clauses.
The article describes an intelligent grading and risk‑assessment framework for commercial platforms that unifies process control, feature mining, data collection, storage, strategy management, and annotation, enabling automated testing conversion with 94% accuracy, 90% recall, 8% conversion uplift, and surfacing dozens of bugs while supporting scalable, configurable deployment.
This article presents a comprehensive overview of technical middle‑platform architectures, covering core platform diagrams, banking data structures, retail industry models, backend design principles, real‑time data pipelines, enterprise evolution stages, and case studies from Alibaba and NetEase, illustrated with numerous diagrams.
This article details the design and implementation of 58.com’s real‑time computing platform, covering its architecture, data ingestion, storage, Flink‑based stream processing, SQL extensions, performance optimizations, Storm‑to‑Flink migration tools, the Wstream management console, state handling, monitoring, and future roadmap.
Linkis, an open‑source computing middleware from WeBank, has entered the Apache Software Foundation Incubator, offering REST/WebSocket/JDBC interfaces to a wide range of engines such as Spark, Hive, Presto and Flink, and providing powerful governance, orchestration, and resource‑management capabilities for big‑data platforms.
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.
This article outlines a typical big data platform architecture, detailing its core layers—data acquisition, storage and analysis, sharing, application, real‑time computation, and task scheduling—while introducing key technologies such as Flume, HDFS, Hive, Spark, DataX, and monitoring considerations.
This article presents an in‑depth overview of NetEase's massive big‑data ecosystem, its diverse product lines, the full‑link data platform matrix, user‑profile taxonomy, ID‑mapping techniques, regional domain insights, tag management, quality assurance mechanisms, and real‑world case studies demonstrating applications in marketing, recommendation, growth, and anti‑fraud scenarios.
The article outlines Alibaba's twelve‑year journey of building a data middle platform, describing four development stages, the technical challenges faced, and emerging trends such as lake‑warehouse integration, autonomous data‑warehouse operation, natural‑language query, and AI engineering.
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 details NetEase Cloud Music's journey of redesigning its data warehouse and governance processes to support over a billion monthly active users, covering pain points, standardization, shared services, self‑service tools, and the resulting improvements in data quality, latency, and operational efficiency.
This comprehensive guide explains the concepts, architecture, and best practices of data middle platforms, covering data aggregation, ingestion tools, offline and real‑time development, data governance, service layers, and implementation details for building scalable big‑data solutions.
The article introduces Kuaishou's Havok data‑service platform—a one‑stop, configuration‑driven solution that lowers development barriers—and details the comprehensive high‑availability architecture, including hierarchical isolation, elastic scaling, link grading, disaster recovery, and rate‑limiting mechanisms that enable zero‑failure support for large‑scale events.
This article introduces Kuaishou's data platform serviceification, outlining the background challenges for data engineers, the platform's architecture and key technologies such as configuration‑driven development, multi‑mode APIs, data acceleration, and high‑availability mechanisms, and concludes with a summary of achievements and future directions.
The Longgong Data Analysis Platform enables Idle Fish to capture, store, and analyze billions of structured product attributes in real time across more than 8,000 categories, using TableStore, MySQL, ODPS, and a distributed scheduler to achieve over 50% query speedup, 80% category coverage, and rapid support for search and recommendation teams.
This article provides a comprehensive technical overview of data middle platforms, covering data aggregation, offline and real‑time development, smart operations, data asset management, governance, service layers, platform implementations, warehouse layering, and key differences between offline and real‑time data warehouses.
This article shares practical notes on building a one‑stop big data platform, outlining essential functions such as data extraction, cleaning, storage, analysis, governance, and security, and presents implementation case studies from WeBank, Beike, and iQIYI to illustrate real‑world architectures and solutions.
This article provides a detailed introduction to data middle platform concepts, covering data aggregation, ingestion tools, offline and real‑time development, data governance, service layers, monitoring, and deployment patterns, illustrating how enterprises build unified data ecosystems across various industries.
The article describes a unified global feature pool architecture that standardizes offline and real‑time feature production, management, and service layers using Hive, Spark, Flink, Kafka, MySQL, and Hologres to break data silos, improve algorithm development efficiency, and boost growth business performance.
This article describes Baidu's quality metric model that integrates development process data, self‑testing and automation data to automate test prioritization, estimate project risk, and improve testing efficiency through a six‑component platform covering process control, feature mining, data collection, storage, strategy management, and annotation.
The talk by Beike senior engineer Yang Zongqiang details the evolution of the company's data development platform, covering background, three architecture upgrades, platform features such as metadata management, data integration, scheduling, quality assurance, and future directions for building an enterprise‑grade big‑data system.
The article details Beike's OLAP platform built on Druid, covering its four‑layer architecture, selection criteria, performance comparisons with Kylin, data ingestion workflows, custom improvements for data import and real‑time distinct counting, and operational measures such as caching, dynamic throttling, and HDFS storage optimization.
This article shares how Beike, a 20‑year‑old traditional real‑estate company, built and evolved its data platform to support digital transformation, describing the business background, data characteristics, user groups, application scenarios, platform challenges, the Odin analysis platform, data‑as‑asset management, and future directions.
This article explains Youzan's data governance framework, covering the definition of data governance, the company's asset‑centric approach, quantitative quality scoring, cost‑based pricing formulas, billing and allocation mechanisms, continuous operational improvements, and the measurable outcomes achieved.
Suning’s Data Middle Platform integrates an accelerated OLAP engine, a star‑schema metric system, a visualization tool built on standardized dimensions, and a unified report portal to solve data silos, improve security, and enable enterprises to evolve into technology‑driven organizations.
The article explains why shifting batch jobs, reporting, and machine‑learning model training from traditional backend services to a dedicated data platform can simplify development, improve fault tolerance, and scale analytics, using real‑world examples from Spotify and best‑practice guidelines.
This article details Minsheng Bank’s data middle‑platform construction, its alignment with cloud‑native principles, the challenges it addresses, and the suite of micro‑service, DevOps and tooling innovations—including a one‑stop DevOps workbench, code generators, automated validation, and full‑link tracing—implemented to support diverse financial data services.
This article explains how Kuaishou’s data‑service platform tackles high development barriers and repetitive work by introducing a self‑service, configuration‑driven architecture, multi‑mode APIs, efficient data acceleration, and robust high‑availability mechanisms, while outlining its evolution and future roadmap.
The article provides a comprehensive overview of the Qirin big‑data platform, detailing its architecture, core modules such as resource management, metadata, data ingestion, task development, interactive query, and self‑service analysis, and outlines future development plans for the system.
The article explains ClickHouse’s high‑performance columnar OLAP design, its vectorized execution, sparse primary‑key indexes and MergeTree engines, contrasts it with ROLAP/MOLAP approaches, and details Youzan’s large‑scale deployment—including dual‑replica clusters, ingestion pipelines, routing architecture, current challenges, and future container‑based expansion plans.
This article reviews the evolution, architecture, and key components of major Chinese big‑data platforms—including those of Taobao, Didi, Meituan, 360, Kuaishou, and JD—highlighting data ingestion, storage, processing engines, scheduling systems, and service‑oriented designs that underpin their large‑scale data operations.
This article explains Kuaishou's data service platform, detailing the background challenges of high development barriers and duplicated work, the platform's architecture and key technologies such as configuration‑driven development, multi‑mode APIs, data acceleration, and high‑availability mechanisms, and concludes with future directions.
The article introduces the Qirin big data platform—a one‑stop solution covering resource management, metadata, data ingestion, task development, interactive querying, and self‑service analysis—detailing its modular architecture, typical processing workflow, and future development plans for enterprise‑wide data services.
This article provides a comprehensive overview of data middle platform concepts, covering data aggregation, collection tools, development modules, job scheduling, baseline control, heterogeneous storage, permission management, real‑time and offline processing, governance, services, and implementation details for building robust big‑data solutions.
This article details Youzan's end‑to‑end construction of a unified data‑center cost billing system, covering background goals, multi‑type cost support, SDK‑based information collection, cost quantification for offline, real‑time and platform tools, full‑business coverage, multi‑dimensional analysis models, operational rollout, and future plans.
This article presents NetEase Yanxuan's comprehensive approach to building a tag and profile middle‑platform, detailing the concepts of tags and profiles, their business value, the platform's architecture and functional modules, and the methodologies for constructing tag systems and applying profiles to drive data‑driven operations.
This article presents 58.com’s large‑scale big data platform, detailing its business scope, the WMDA one‑stop analytics system, the Wanxiang user‑portrait service, the technical challenges of massive daily data ingestion, multi‑dimensional analysis, OLAP engine selection (Kylin, Druid), bitmap‑based user‑group processing, scheduling, and overall data service architecture.
This article outlines the step‑by‑step approach to constructing a big data platform—starting with script toolization, evolving through tool services, platformization, and productization, comparing business‑scenario and generic‑component construction methods, and detailing the Lambda architecture for data collection, processing, and visualization to drive business operations.
This article explains the concept of a middle platform, its purpose in improving system reuse and user responsiveness, the reasons for platformization, the distinction between front‑end, back‑end and middle platforms, and how business and data middle platforms can be evaluated and operated within modern enterprises.
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.
The article summarizes Beike's one‑stop big data development platform, describing its data business background, the evolution from a simple Hadoop‑Kafka‑Hive stack to a metadata‑driven, asset‑oriented platform, and outlines current capabilities in data management, integration, scheduling, quality, openness, and future plans.
This article explains why Yanxuan, a brand e‑commerce platform, created its own omnichannel user‑reach system, outlines the business problems it solves, describes the four‑layer architecture—including data, AI, application, and business layers—and shares early results and future work.
This article examines Yanxuan’s transition to an AI‑driven omnichannel user‑reach system, detailing the business challenges of fragmented channels, the four‑step intelligent workflow, and a four‑layer architecture that integrates data platforms, algorithm services, applications, and business operations to maximize ROI.
This comprehensive article explains the concept of the mid‑platform (Zhongtai), its origins, classification into business, data, algorithm, technical, R&D, and organizational platforms, examines the challenges it solves, and reviews real‑world implementations by Alibaba, Huawei, NetEase, Baidu, and Tencent, concluding with practical guidance for adopting a suitable platform strategy.
This article examines how O2O ride‑hailing platforms such as Uber and Didi rely on layered system architectures and AI‑driven data middle platforms to enable intelligent dispatch, dynamic pricing, and safety mechanisms, detailing the core components, matching algorithms, and machine‑learning models that power these services.
This article examines how O2O ride‑hailing services like Uber and Didi rely on layered system architecture and algorithmic data platforms to enable intelligent matching, dynamic pricing, and safety mechanisms through machine‑learning models and real‑time data integration.
OPPO's data platform engineer Zhang Jun shares the design and implementation of OPPO's real‑time data warehouse built on Apache Flink, covering background, top‑level architecture, practical deployment, and future directions such as enhanced SQL development, resource scheduling, and automated configuration.
The article explains how implementing business and data middle platforms can enhance enterprise capabilities, streamline processes, foster innovation, and provide strategic advantages for leaders, managers, IT teams, and developers by centralizing standards, enabling data-driven decision‑making, and reducing operational costs.
To support Didi’s fast‑growing car‑pool service, a real‑time data warehouse was built using a streamlined layered architecture—ODS, DWD, DIM, DWM, and APP—leveraging Flink‑based StreamSQL, Kafka, Druid and ClickHouse to deliver minute‑level analytics, dashboards, monitoring, and cross‑business interfaces while planning unified meta‑store integration.
This talk presents the architecture, feature extraction, storage, real-time computation, monitoring, and optimization strategies of 58.com’s commercial DMP platform, detailing business requirements, system design across data, storage, compute, and service layers, and future plans for unified services and advanced analytics.
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.
This article recounts SuNing’s step‑by‑step journey of designing and implementing a supply‑chain data middle platform, outlining its business rationale, DDD‑based domain modeling, layered system architecture, and practical deployment insights that illustrate how a tailored big‑data solution can enhance data services and governance.
