The article recounts NetEase’s presentation at the inaugural DataOps conference, detailing the evolution from DataOps 1.0 pipeline to a 2.0 integrated data development‑governance model, the challenges faced, practical solutions, and strategic advice for data managers.
This article explains what data lineage is, its components, why it matters for data quality, security, and operational efficiency, and provides a comprehensive implementation guide covering open‑source tools, commercial platforms, custom builds, graph‑database modeling, automatic and manual lineage capture, visualization, analytics, and evaluation metrics.
This analysis examines why Chinese state‑owned enterprises face unclear digital‑transformation goals, weak strategic positioning, fragmented data, talent shortages, and inadequate technology ecosystems, and it outlines the root causes, typical case studies, and recommended actions to achieve effective digital change.
The article analyzes why government data sharing often fails—citing legal, technical, security, and organizational hurdles—then outlines one‑to‑one and centralized sharing models, highlights four critical success factors, and proposes a “three‑rights” framework supported by blockchain to create trustworthy, sustainable inter‑departmental data exchange.
This article details how Wing Pay built a comprehensive data development and governance platform, covering company background, business scenarios, goals, challenges, task development workflow, task types, SparkSQL editor features, double‑environment deployment, Airflow scheduling, DataX data bus, resource isolation, compute optimization, data quality monitoring, cloud‑native practices, future outlook, and a Q&A on data permissions and governance.
Data governance, the overarching framework for evaluating, guiding, and supervising an organization’s data lifecycle—from collection to utilization—ensures high data quality, compliance, and security, ultimately maximizing data value and supporting AI-driven initiatives, while distinguishing itself from data management and data control through a strategic, top‑down approach.
In this interview, three data‑governance experts from Tencent, ByteDance, and Alibaba discuss the fundamental processes, core domains such as metadata, data lineage, metric systems, data quality and security, the main challenges they face, and emerging trends like DataOps, AI‑driven automation, and privacy‑preserving technologies.
This article examines two decades of data warehouse evolution in the financial sector, identifies persistent pain points such as platform lag, data quality, and low service efficiency, and proposes a cloud‑native, data‑centric framework—including a unified blueprint, three‑layer architecture, and six core capabilities—to accelerate enterprise‑wide data capability building and drive high‑quality digital growth.
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 introduces the Arctic lakehouse management system built on Apache Iceberg, explains Iceberg’s core principles, format versions, and real‑world implementations at NetEase, and details Arctic’s automated table optimization, governance workflows, and future development plans.
This article explains why data quality is critical for businesses, outlines common data quality problems, their root causes, and presents a comprehensive governance framework—including monitoring rules, alerting, full‑link monitoring, and a seven‑dimensional evaluation model—to ensure high‑quality data delivery.
This article presents Dipu Technology's comprehensive data governance methodology, covering construction paths, a typical enterprise digital platform framework, core governance components, practical case studies, and a Q&A session that together illustrate how businesses can design, implement, and sustain effective data governance across the organization.
This article details Bilibili's evolution of big data governance, describing the early data growth challenges, the launch of the "Wanglou" project, the development of asset metadata and governance indicator frameworks, storage cost reduction strategies, scoring models, and the shift from passive, single‑point fixes to proactive, multi‑dimensional governance across the organization.
The article presents Dipu Technology's comprehensive approach to data governance, outlining construction pathways, a typical enterprise digital platform framework, core governance concepts, implementation steps, case studies, and a Q&A session that together illustrate how to design, execute, and sustain effective data governance across business domains.
This article presents a detailed roadmap for enterprise data governance, covering business digitization goals, data governance construction, typical digital platform architecture, core governance actions, implementation pathways, data asset inventory techniques, and real‑world case studies to illustrate practical execution.
This article outlines a complete data asset management lifecycle—starting with data inventory, moving through governance, classification, responsibility, permission, and security, and culminating in value realization via basic services, profiling, and algorithmic models—providing practical guidance for building a robust big‑data platform.
This article explains the importance of metric management for unified data language, consistent data production, and increased metric usage, and outlines a three‑part system covering business‑process metricization, standardization of naming, scope and lifecycle, and operationalization of metrics.
This article details Bilibili's journey from a late‑started, reactive big‑data platform to a mature, proactive governance system that combines asset metadata, metric‑driven strategies, cost‑aware billing, and automated tooling to achieve massive storage savings and operational efficiency across the organization.
This article explains the security risks of enterprise data platforms, analyzes gaps in traditional protection methods, and presents a comprehensive three‑layer security architecture—asset, capability, and control layers—along with pre‑, during‑, and post‑process measures to ensure data safety throughout its lifecycle.
This article details DataCake's data‑governance journey, covering the problems of data silos, unclear costs, and tool fragmentation, then explains the strategic thinking, the multi‑layered solution architecture, and the measurable outcomes such as higher resource utilization and reclaimed storage.
Bilibili’s exabyte‑scale big‑data platform, after rapid growth created fragmented ownership and costly storage, launched the Wanglou project to build a metadata‑driven, indicator‑based governance framework that cut storage use by half, introduced compliance scoring and automation, and now plans to extend proactive, multi‑dimensional governance to compute, traffic and lake‑house resources.
In the big‑data era, high‑quality data are essential for reliable analytics, and this article explains data‑quality concepts, key dimensions, analysis methods for missing values, outliers, inconsistencies and duplicates, as well as practical management practices to ensure data assets become a competitive advantage.
The article introduces DataCake, a cloud‑native, multi‑cloud big data platform built by SHAREit Group that addresses massive data volume, diverse application scenarios, and governance challenges through a Data Mesh‑inspired self‑service architecture, offering unified data management, intelligent governance, and a roadmap for future enhancements.
This article explores the key problems data governance aims to solve, outlines a comprehensive governance framework, and details practical implementation steps—including tool integration, metadata management, lake‑in and lake‑out processes, and governance policies—to achieve a closed‑loop, value‑driven data ecosystem.
The 2023 DAMS China Data Intelligence Management Summit in Shanghai featured a technical presentation by Zhang Shenbo on an open‑source SQLE solution for SQL quality control, covering multi‑database auditing, automated review workflows, and practical tips to reduce DBA workload and cross‑department communication.
This article explains how modern commercial banks can turn massive operational data into a strategic asset by building a comprehensive data governance framework that addresses data standards, quality management, metadata, master data, and security, while outlining a six‑step methodology for continuous improvement.
China Merchants Bank became the first among China's top system‑important banks to fully migrate all debit, credit, corporate accounts and applications to the cloud, detailing the multi‑year effort, architectural overhaul, cost savings, and the strategic shift toward open, distributed systems.
This article explains how effective data governance transforms raw data into a trusted enterprise asset, outlines common pitfalls such as backward and passive governance, and presents a structured, four‑phase approach—including organizational setup, standards, platform selection, and continuous operations—to successfully implement data governance at scale.
This article examines ByteDance's data governance journey, outlining business, organizational, and cultural challenges, the six-stage evolution framework, real‑world case studies, and the shift from centralized to distributed autonomous governance to improve quality, security, cost, and team efficiency.
The article explains that while big data aims to boost organizational insight and innovation, its true value depends on high data quality, outlines industry standards, identifies technical, business, and management causes of poor quality, and proposes a three‑phase strategy of prevention, monitoring, and post‑improvement to ensure reliable data for decision‑making.
Airbnb’s Data Protection Service unifies privacy and security metadata, offering APIs that automate annotation verification, export and IDL validation, data‑subject‑rights orchestration, and secret‑leak detection, while assigning ownership, minimizing manual effort, and ensuring global, consistent compliance across the platform.
This article explains why data governance matters for small‑to‑medium enterprises, outlines its four key values, describes essential governance components, and provides a step‑by‑step framework—including timing, roles, standards, execution mechanisms, tools, and common pitfalls—to help organizations implement effective data governance.
The article examines how China's data middle platform concept is reshaping enterprise data strategy, highlighting a shift toward value‑driven adoption, the intertwined relationship with data governance, and emerging trends such as fine‑grained business governance, full‑link monitoring, integrated platforms, and DataOps.
In this interview, ICBC senior manager Wei Yadong shares the bank's challenges with massive data, the five‑point criteria for database selection, the DevOps‑driven MySQL governance framework, evolving security demands, future database trends for finance, and practical advice for database professionals.
This whitepaper explains why effective BI governance is essential for modern enterprises, outlines the key capabilities of data‑governance tools—including data quality, certification, usage statistics, classification, lineage, glossary, and lifecycle management—and shows how BI portals and data catalogs together enable scalable, user‑centric analytics.
DataOps, rooted in agile and DevOps philosophies, uses automation and collaborative practices to streamline data processing, improve quality, and align analytics with business goals, offering continuous analytics, faster insights, and breaking data silos for better decision‑making across organizations.
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.
