This article presents Datacake's experience of integrating AI algorithms into big data governance, covering the bidirectional relationship between AI and big data, health‑score assessment of data tasks, intelligent Spark parameter tuning, SQL engine selection, and future application scenarios across the data lifecycle.
Baidu's Performance Platform speeds data production and consumption by adopting a unified stream‑batch architecture with TM and Spark, leveraging the Turing warehouse, introducing tiered service grading, robust governance and compliance measures, and offering self‑service analytics, cutting latency from minutes or days to milliseconds while handling billions of daily records and boosting SLA adherence, data accuracy, and user satisfaction.
This article explains the core principles, strategies, frameworks, and governance practices of data management based on DAMA's DMBOK, covering data lifecycle, value, leadership responsibilities, strategic planning, governance models, metrics, and implementation guidelines to help organizations derive business value from high‑quality data.
The article details Zhongyuan Bank's comprehensive data security governance, covering the regulatory background, protection objectives, classification of data assets, organizational and procedural management mechanisms, technical safeguards across the data lifecycle, and future planning to enhance compliance and risk mitigation in the banking sector.
This article provides a detailed overview of data middle platform concepts, describing a decoupled six‑subsystem architecture—including storage, collection, processing, governance, security, and operation frameworks—while illustrating typical enterprise implementations, industry‑specific solutions, and best‑practice considerations for building scalable, secure, and value‑driven data platforms.
This article presents an in‑depth overview of DataLeap's data lineage capabilities, covering the challenges, multi‑layer model design, implementation with Apache Atlas and JanusGraph, performance optimizations, diverse use cases across asset, development, governance and security domains, and future trends for lineage technology.
This article details Zhongyuan Bank's AI platform construction, covering its objectives, MLOps-driven design, core modules such as data ingestion, processing, model development, training, evaluation, deployment, monitoring, as well as resource orchestration with Kubernetes and Docker, and the accompanying ModelOps governance framework.
Yidian Tianxia tackled massive daily data volumes and complex analytics by defining a five‑layer data‑warehouse standard, comparing ClickHouse and StarRocks performance, and implementing a unified real‑time/offline architecture with StarRocks, DataPlus, and EasyJob, achieving multi‑fold query speedups and lower operational costs.
This article explores Alibaba's DataWorks platform and its comprehensive data governance practices, covering application efficiency, security controls, cost optimization, organizational structure, and cultural initiatives that together enable scalable, secure, and cost‑effective data management across the enterprise.
The article outlines Xiaomi's data governance journey, focusing on storage‑service cost governance, describing the transition from simple cost‑centered governance to big‑data‑driven asset management, and detailing concrete HDFS and HBase practices that achieved significant resource and cost reductions.
Alibaba’s DataWorks team shares extensive experiences in building and operating a large‑scale data platform, covering data governance across stages—from data stability and quality to security, cost control, and organizational culture—illustrating how systematic practices and tools drive efficiency, reliability, and value for enterprises.
This article details Cloud Music's year‑long data assetization journey, covering the background, practical achievements, governance methods, and future roadmap for turning massive data warehouses into high‑value, well‑governed assets that drive cost reduction and business insight.
This article examines the inherent complexity of building big‑data platforms, compares the emerging concepts of Data Fabric and Data Mesh, outlines their architectural features, technology stacks, and practical implementation challenges, and offers guidance on when each approach is appropriate.
This article presents a curated collection of big‑data learning resources, including interview guides, in‑depth articles on Flink, Spark, Hive, ClickHouse, data governance, and personal growth, offering readers a one‑stop reference to boost their big‑data expertise and interview readiness.
This article explains how data standards form the foundation of data governance, clarifies data assets, breaks silos, accelerates data flow, and outlines their definitions, benefits, common challenges, essential components, and best practices for effective implementation.
This article presents a curated collection of the most comprehensive big‑data interview preparation resources, including expert guides, tutorials, and deep‑dive articles on Flink, Spark, Hive, ClickHouse, data governance, and related topics, accompanied by a call to engage with the content.
This article shares practical insights from a data product expert on the problems faced by content‑oriented data products, outlines a comprehensive governance methodology—including DAMA, Huawei, and Alibaba frameworks—and demonstrates how to operationalize these ideas through concrete examples such as event‑tracking and metric governance.
Youzan built an automated offline data cost‑optimization platform that gathers accurate metadata, mines unused or failing tables and tasks, and safely decommissions them through a backend‑frontend workflow with owner validation, notifications, rollback safeguards, and plans to extend lineage coverage and real‑time asset handling.
This article outlines Tencent's data governance challenges, its internal three‑stage practice, detailed case studies such as Tencent News and PCG cost governance, and introduces the WeData platform's architecture and tools for standardization, quality, security, and metadata management, concluding with a Q&A session.
The article announces Apache Linkis’s graduation to an Apache top‑level project, explains its role as a computing middleware linking applications to engines like Spark, Hive, and Flink, details its core capabilities, roadmap, ecosystem integrations, and provides official resources for the community.
This article explains how effective metadata management provides context, improves data quality, enables data lineage tracing, supports governance, and ultimately turns raw data into valuable assets for enterprises navigating complex, evolving data environments.
This comprehensive guide walks you through the entire data governance ecosystem, covering metadata fundamentals, classification, maturity models, data standards, modeling, integration, lifecycle management, quality assurance, security, and ETL processes, all illustrated with clear diagrams and practical steps.
This article presents Tencent's comprehensive data governance framework, covering its definition, objectives, challenges, methodology, organizational structure, metadata management, data asset lifecycle, security measures, and technical implementation details such as microservice architecture, data collection, lineage analysis, and storage solutions.
The article explains why data governance is essential for high‑quality data in big‑data organizations, outlines narrow and broad governance scopes, presents strategic principles, and shares eight detailed case studies from leading Chinese tech companies illustrating practical implementation and lessons learned.
Enterprises must shift their perception of data assets and embed data‑value into every digital process, establishing governance, unified asset catalogs, operational metrics, security controls, integration, services, and visualization to transform raw data into strategic business outcomes.
The article explains the importance of data governance, distinguishes narrow and broad governance, outlines strategic principles such as systemic engineering and prioritization, and presents eight case studies from leading Chinese tech companies illustrating practical implementations and effective strategies.
This article outlines Tencent's data governance challenges, internal practices across three maturity stages, and introduces the WeData platform that provides comprehensive capabilities for data assetization, cost control, quality assurance, security, and metadata management to support large‑scale big‑data operations.
This article explains why data is a company's most valuable asset, distinguishes narrow and broad data‑governance approaches, outlines strategic design principles, and presents eight detailed case studies from leading Chinese tech firms illustrating practical governance implementations and lessons learned.
This article outlines the evolution of data management in the big‑data era, covering the history of the industry, key governance frameworks such as DMBOK, DCMM and DMM, the steps to construct a data‑management system, the requirements for a data‑factor market, and an introduction to the DataEasy company and its services.
At the 2022 DataFun Summit, data governance experts from Huolala, Zhongtong, and SF Express shared comprehensive practices—including governance drivers, quality monitoring, model management, master data processes, platform architecture, cost control, and stability measures—illustrating how large logistics firms implement end‑to‑end data governance to boost efficiency, compliance, and business value.
Lean Data Methodology combines lean thinking, design thinking, the Cynefin framework, and agile principles to create a data‑driven digital transformation system that defines value, eliminates waste, and equips enterprises with strategic, product, governance, collaboration, platform, and cultural capabilities for building lean digital enterprises.
This article explains how companies can transform data assets into operational power and market revenue by establishing data sharing platforms, leveraging analysis tools, building data products, and ensuring security and compliance throughout the data lifecycle.
This article explains the importance of data governance, distinguishes narrow and broad governance, outlines its systemic and selective nature, and presents eight practical case studies from companies like Tencent, NetEase, and MobTech, offering actionable strategies for high‑quality data across its lifecycle.
DataOps, the data‑centric counterpart of DevOps, combines agile principles, standardized tools, and cross‑team collaboration to manage the full data lifecycle—from integration and development to storage, governance, and service—enabling organizations to handle massive, diverse datasets efficiently, reduce silos, and turn data into actionable value.
This article explains how companies can transform collected data into valuable assets by addressing data assetization challenges, building comprehensive data platform functions, and developing team capabilities across governance, quality, security, and operational domains.
The article outlines JD's data‑driven business development strategy, describing the current challenges of its business data marketplace, the governance framework—including layered architecture, standardization, ClickHouse dictionary refresh, and optimization measures—and the resulting performance improvements and future outlook.
This article describes ByteDance's comprehensive approach to managing the massive volume of event‑tracking (埋点) data, detailing the background, cost‑reduction strategies, experience review, future plans, and a Q&A session that together illustrate how systematic data governance can dramatically cut storage and processing expenses.
The article uses a dating analogy to illustrate how enterprise data strategy must combine clean, high‑quality data, governance, and analytics with clear tactical components to support strategic goals, drive market advantage, and enable reliable, mission‑focused outcomes in the experience economy.
The interview gathers insights from data‑platform experts on the maturity stages, technology trends, implementation methodologies, open‑source ecosystems, system architectures, governance, security, and assessment criteria of modern data middle platforms, offering a comprehensive guide for practitioners.
Meituan’s technology team highlights its twenty most‑read 2022 blog posts, spanning observability, system design, data governance, AI, cloud‑native engineering, and practical innovations such as visual log tracing, Kafka scaling, functional programming, Elasticsearch optimization, CI/CD pipelines, and advanced object‑detection frameworks.
The article explains how product and business teams should collaborate with data teams to build and promote data metric systems, emphasizing mutual empowerment, joint methodology, pilot testing, and scaling, while also announcing DataFun's 5‑year anniversary activities and upcoming big‑data and AI publications.
This article explores DataOps from its historical evolution through the digital 3.0 era, outlines its core technology stacks such as Data Fabric, Data Mesh, and Modern Data Stack, and demonstrates practical applications across finance, manufacturing, telecom, and public services, highlighting its role in agile, cloud‑native data management.
Kuaikan Manhua ensures continuous data governance by establishing a closed‑loop of business scope management, data asset standards, and feedback mechanisms that keep data pollution slower than governance speed, enabling systematic, long‑term data quality improvement.
This article explains ByteDance's end‑to‑end tracking data lifecycle management, including pre‑report validation, the rationale for using BMQ over Kafka, quality governance examples, and how Flink‑based pipelines ensure data accuracy through SLA monitoring and checkpoint strategies.
This article outlines a comprehensive data quality management framework, covering expectations, measurement, assurance, and operational practices, and provides concrete templates, rule designs, and governance processes to help data teams systematically assess, monitor, and improve data reliability throughout the lifecycle.
This article presents Douyu Live's experience in building a data middle platform, outlining the challenges of data application, the four‑stage evolution of their data tools, current achievements, and future goals to empower every employee as a data analyst.
iQIYI adopts SphereEx’s Database Mesh 2.0 by extending ShardingSphere‑JDBC and integrating the cloud‑native Pisanix proxy, creating a unified, encrypted configuration center that enables dynamic sharding, load‑balancing, read‑write separation, hot‑updates and observability, dramatically simplifying database governance and cloud migration.
Data preparation consumes about 80% of the entire analytics workflow, making data collection, quality assurance, and governance critical pillars—spanning metadata, master data, storage layers like data lakes and warehouses, and rigorous preprocessing—to turn raw information into reliable insights.
This article presents an in‑depth overview of 58.com’s self‑built Star River big data platform, covering its evolution across three eras, resource management hierarchy, core technical capabilities such as metadata services, data maps and lineage, governance practices, and the roadmap for further enhancements.
The article explains how cloud‑native architectures, data governance, intelligent fusion, and privacy computing are driving the evolution of big data, recounting the history from Google’s early papers and Hadoop to modern managed services, compute‑storage separation, AI‑powered recommendation platforms, and real‑world success cases.
This article details NetEase DataFan's journey in building a full‑stack big‑data platform, explains the design‑first data‑mid‑platform approach, analyzes cost, quality, and security problems encountered, and presents the modern data‑governance framework that integrates development, governance, and consumption into a closed loop.
This article details NetEase Cloud Music's year‑long data governance initiative, covering data background, governance strategy, project plan, practical actions, results, and future outlook, and shows how metadata‑driven management reduced storage by over 30% while improving reliability and efficiency.
The article outlines a complete data‑quality‑management framework that first captures business expectations, then translates them into basic and personalized measurement rules, defines four assurance approaches for handling violations, and scales operation with indicators, tooling, and metrics to continuously improve data quality across the lifecycle.
This article explains data governance fundamentals, outlines common evaluation shortcomings, and introduces observability concepts and the GuanCe Cloud platform as a way to objectively measure and improve governance outcomes across the entire data lifecycle.
This article explains what data governance is, why enterprises need it, the key components such as data quality, metadata, master data, asset and security management, and provides a step‑by‑step framework, organizational structure, platform features, evaluation methods and common pitfalls.
This article explains why data quality is critical for business success, outlines common data quality problems and their root causes, and presents a practical governance framework with monitoring rules, alerts, full‑link monitoring, and a seven‑dimensional evaluation model to continuously improve data reliability.
This article outlines the background and terminology of data warehousing, presents a formula for evaluating warehouse value, and details the team's half‑year efforts—including architecture selection, quality assurance, stability governance, and data‑value externalization—to improve efficiency, quality, stability, and cost in real‑time data services.
This article explains the concept of metadata, its role in data warehouses, why managing metadata is critical for building, maintaining, and scaling data warehouse systems, and outlines practical steps, use cases, and tools for effective metadata management.
Industrial data governance, essential for digital transformation, faces challenges such as data heterogeneity, volume, quality, and integration across the value chain, and the presentation outlines background, practical approaches, strategic thinking, and a phased, demand‑driven model to enhance data quality, assetization, and business value.
This article details SF Express Technology’s decade‑long data governance journey, outlining its three‑phase evolution, comprehensive framework, key policies, organizational structure, and practical implementations such as master data management, data quality, metadata, data market, and security, highlighting lessons and best practices for enterprise data management.
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 article details YiPay's big data BI analysis platform construction, covering its financial data use cases, platform architecture, OLAP engine implementations with ClickHouse, Presto, and Kylin, as well as identified challenges and future development directions.
This article details Huolala's end‑to‑end data governance practice, covering the construction of a data governance framework, the implementation of a zero‑code data quality platform, a metadata management platform, and a cost‑governance system that together improve data reliability, reduce waste, and support scalable big‑data operations.
The article outlines comprehensive data‑engineering and warehouse‑design principles—covering collection (four Ws and methods like SDK, point‑code, binlog), reporting strategies, source selection, modeling with fact, aggregation, dimension and model tables, quality checks, and governance practices such as standardized SDKs, metric libraries, automated lineage, and cost optimization—to share actionable experience for any organization.
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 State Grid Tianjin Electric Power's early adoption and successful certification of the national DCMM data management maturity model, outlining background, certification milestones, systematic practices, and lessons learned that illustrate how data governance, architecture, and application strategies drive digital transformation.
This article details the practical framework, policies, tools, and workflows that a major online medical service used to classify and grade its data assets, ensuring compliance with China's Data Security Law and Personal Information Protection Law while reducing business risk.
This article presents an in‑depth overview of Apache Iceberg as used at Tencent, covering its table format architecture, Spark read/write mechanisms, production challenges and optimizations such as schema evolution, file filtering, upsert strategies, and the surrounding data‑governance services.
This article explains DataOps as a methodology for improving data analysis quality and efficiency, outlines its origins, standards, and maturity model, and presents practical insights and case studies from Chinese enterprises on how DataOps addresses common data engineering challenges and drives digital transformation.
Amid the explosive growth of big data, this article explains why systematic data governance—covering metadata, quality, lifecycle, and security—is essential for turning raw data into measurable business assets, reducing costs, and enhancing operational efficiency.
This article explains how metadata serves as the foundation of enterprise data governance, outlines common data governance challenges, describes Wing Payment's metadata governance framework and platform architecture, and presents future directions such as multi‑source management, cross‑cluster disaster recovery, and intelligent recommendation.
This article details Kuaishou's end‑to‑end big data platform, describing its organizational model, unified data governance framework, comprehensive data‑quality solution, the design of a headless metric platform, key technologies such as automatic modeling and code generation, and future directions toward a decentralized, smart data fabric.
The article details Tencent Music's data governance journey, describing the background of rapid resource growth, challenges in cost management, a multi‑layered governance methodology—including metadata, tiered storage, and a Lego metadata platform—and the resulting improvements in resource utilization and data quality.
This article outlines Kuaishou's evolution of metadata management from its early Hive‑centric stage to a unified 2.0 platform, detailing system architecture, key technologies, challenges, and future 3.0 vision for low‑code, automated, and intelligent data governance.
This article explains the growing importance of data lineage in large data warehouses, evaluates three Spark lineage extraction approaches, and provides a detailed, step‑by‑step guide to integrating the open‑source Spline agent—including codeless and programmatic initialization, configuration, dispatcher setup, post‑processing, and known limitations.
The article explains how tokenization transforms data security into a built‑in attribute that automatically scales with data growth, detailing its design principles, generation methods, architectural layers, security safeguards, and practical engineering experiences to address exposure risks in modern digital businesses.
This article walks you through the fundamentals of data governance, explains metadata management concepts, compares traditional tools with DataHub, and provides a step‑by‑step tutorial for installing Docker, Python, and DataHub 0.8.20 on CentOS 7, ingesting MySQL metadata, and exploring the UI.
This article presents a comprehensive overview of Taobao's data model governance, detailing the background and problems of the current data architecture, analyzing root causes, proposing a structured governance framework with DataWorks automation, and outlining future plans to improve efficiency, standardization, and product tooling.
This article presents an overview of data governance concepts, common enterprise challenges, and Alibaba's comprehensive data governance framework, covering theory, demand layers, practical solutions for stability, quality, standards, security, cost control, and the supporting platforms and operational practices.
This article explains why event‑tracking (埋点) governance is essential, outlines the methodology and practice of full‑link tracking management, and introduces the one‑stop tracking platform with its innovative features such as standardized processes, verification tools, real‑time dashboards, cross‑platform data unification, and future roadmap.
The talk outlines KuaiKan's data governance journey, describing the rapid business growth challenges, the three‑step logical closed‑loop framework, practical experiences in business scope management, data asset governance, collaboration techniques, and future outlook, highlighting evaluation metrics and ongoing improvements.
In the era of big data, poor operations data quality undermines analytics, decision‑making and digital transformation, so organizations must adopt a three‑dimensional governance approach—covering organization, processes and technology—to ensure completeness, consistency, accuracy, uniqueness, relevance and timeliness of their operational data.
This article outlines Snowball's data warehouse background, compares major modeling approaches such as ER, dimensional, DataVault and Anchor models, describes the current challenges of their dimensional model, and details the OneData methodology—including OneModel, OneID, and OneService—along with its practical implementation, results, and future plans.
The presentation details how Amazon Web Services’ intelligent data lake architecture integrates big data and machine learning to overcome marketing challenges, improve data governance, and provide scalable, real‑time analytics for personalized, data‑driven marketing across enterprises.
This article provides a comprehensive overview of data lakes, explaining their definition, key characteristics, architectural evolution, and detailed comparisons of major cloud providers' solutions, while also presenting typical use cases, construction processes, and future development directions for this emerging big‑data infrastructure.
This article explains why event‑tracking data governance is essential, outlines the methodology and practical steps, and introduces a one‑stop platform that standardizes data collection, validation, monitoring, and visualization to improve data quality, efficiency, and business insight.
The author shares a candid account of transitioning from a non‑data role to confronting data‑centric bottlenecks, describing the current state of data projects, common pitfalls, and practical thoughts on simplifying data governance within limited resources and budget constraints.
This article explains how enterprises can implement data governance to protect data throughout its lifecycle—collection, storage, processing, and deletion—especially in public and hybrid cloud settings, outlining SABSA categories, key questions, and practical considerations for secure data management.
This report details Alibaba’s large‑scale data model governance initiative for the DaTao ecosystem, analyzing current data issues such as naming inconsistencies, low reuse, and application‑layer inefficiencies, and presents a comprehensive solution—including a model evaluation system, DataWorks co‑development, intelligent modeling, data map enhancements, and future roadmap—to improve data health, reduce costs, and increase operational efficiency.
This article summarizes Guo Jinshi's presentation on Taobao's data model governance, covering the current data landscape, identified problems, analysis of root causes, proposed governance solutions—including DataWorks intelligent modeling—and future plans, while also providing a Q&A session on practical implementation.
Wei Tao, Vice President of Ant Group, outlined the transition to a data‑confidentiality era, emphasizing the need for privacy‑computing security grading, technical requirements, and industry collaboration to safely circulate data as a new production factor in the post‑2022 big data landscape.
TikTok announced a complete migration of its US user data to Oracle Cloud Infrastructure to address security concerns raised by US officials, outlining the partnership's history, political pressures, and the ongoing effort to safeguard American user information.
This document presents a comprehensive overview of the traffic domain in a data warehouse, covering its concepts, objectives, guiding principles, core and extension models, data quality, monitoring, scheduling, and operational practices to achieve a complete, accurate, efficient, low‑cost, and high‑value traffic data system while addressing massive data volume, consistency, and SLA challenges.
This article explains the concept of data governance, outlines ByteDance's four‑mission platform goals, details the end‑to‑end governance workflow, and describes the one‑stop, full‑link, and rule‑based architecture that powers their data governance solution.
The article presents NetEase's internal data governance experience, outlining past challenges, current pain points, a comprehensive product strategy covering scope, value quantification, and feature implementation, and shares initial results and future plans to build an automated, end‑to‑end big‑data optimization platform.
The article details how Bilibili built a unified metadata infrastructure—including a URN‑based model, collection pipelines, quality assurance, storage in TiDB/ES/HugeGraph, and query services—to support data discovery, lineage, impact analysis, and governance across its growing data platform.
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.
The article explains data governance concepts, outlines four common governance models, details key policy elements such as availability, quality, integrity, usability, and security, and highlights the benefits, risks, and best‑practice recommendations for implementing effective data governance in organizations.
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.
The Meituan Accommodation data governance team shares how they evolved from ad‑hoc, single‑point fixes to a systematic, automated governance framework—covering management, standards, capability, execution, evaluation, and vision—using standardization, digitization, and systematization to achieve measurable quality, cost and efficiency gains across thousands of data assets.
