This article details the background, challenges, design principles, and implementation of a unified data service platform—including data modeling, multi-source governance, real-time processing, and a Doris-based storage solution—to support large‑scale video data for a new media operation.
The article explores how enterprises can adopt domain‑specific large language models by addressing talent and cost challenges, outlining training pipelines, data governance for unstructured data, dataset balancing, fine‑tuning techniques, and a product ecosystem that lowers deployment barriers while optimizing performance and economics.
Enterprises face significant hurdles in data governance and integration, from siloed systems and unclear responsibilities to poor data quality, but by establishing clear rules, fostering user department engagement, and aligning governance with business-driven data applications, they can create a cohesive data asset management framework that drives value.
This article details NetEase EasyData's evolution and product matrix, explains why a metric middle platform is needed, describes its core technical architecture—including a unified logical semantic model, a custom metric query language, and engine decoupling—and outlines future development directions.
The article reviews Anthony J. Algmin's insights from the DATAVERSITY conference, highlighting corrected past predictions, current hot topics such as cloud, AI, and data governance, and future directions including blockchain, metadata management, and the evolving role of data architects.
This article presents Xiaomi's comprehensive metric system framework, covering its definition, business pain points, the OSM and MECE methodologies, model design principles, data warehouse construction, metric management, and future outlook, illustrating how a unified data platform drives efficient business decision‑making.
This article outlines a real‑world enterprise master data project, detailing the definition of master data, the four critical data‑quality challenges faced, the comprehensive solution framework with executive backing, and the six measurable outcomes that improved data governance, efficiency, and decision‑making across the organization.
The article presents Tencent PCG's comprehensive data governance solution, detailing the challenges of massive, heterogeneous data, the four‑chapter framework covering governance overview, meta‑warehouse construction, an open asset‑scoring system, and a one‑stop governance workbench, and explains how lineage, scoring, and rule‑engine mechanisms enable cost‑effective, continuous data governance.
This article examines the challenges faced by Volcano Engine's DataLeap in computational governance, outlines automated solutions such as real‑time rule engines and monitoring, and presents concrete performance and cost benefits achieved through resource optimization across large‑scale Spark and Hadoop workloads.
This article explains why enterprises need a data asset framework, outlines its key components such as catalog management, policy support, and development trends, and provides a step‑by‑step guide with visual diagrams for constructing and operating a comprehensive data asset management system.
This article explains the challenges of data asset lineage in large data warehouses, presents a comprehensive approach using business‑level instrumentation, SQL interceptor plugins, and ETL script parsing to generate fine‑grained lineage graphs, and demonstrates measurable improvements in coverage and zombie‑table cleanup.
This article examines why data governance has become a critical pillar for Chinese financial institutions, outlining external regulations and internal business drivers, describing a comprehensive governance architecture, and presenting a detailed case study of a securities company's data‑asset inventory, platform implementation, and quality management.
The MIKE2.0 method is an open‑source, integrated‑knowledge‑environment framework that guides enterprises through information development, data governance, and architecture via five structured phases, key structures such as SAFE, and practical task outputs, while also offering community resources and implementation guidance.
This article presents a comprehensive overview of Taobao's data model governance, covering background challenges, a four‑pillar solution framework, detailed practices such as invalid table decommissioning, source‑table consolidation, data handover, public‑layer operations, incremental control, productization, and future planning to improve efficiency, cost, and quality of large‑scale data models.
This article outlines the challenges of ultra‑large e‑commerce data warehouses—such as SLA pressure, model instability, soaring resource costs, low governance efficiency, and fragmented processes—and presents a one‑stop, tiered data‑governance framework with stability, cost, and efficiency subsystems that drives distributed autonomous governance and measurable business value.
This article details Huolala's user event tracking (埋点) system, covering its background, challenges, the construction of a four‑module management platform, backend SDK design, monitoring and quality assurance mechanisms, and future plans for service integration, data lineage, and governance optimization.
Low‑quality data can cost enterprises up to $15 million each year, making data quality management essential for accurate decision‑making, compliance, and operational efficiency, and this article explains its importance, evaluation dimensions, common issues, monitoring metrics, responsible roles, and a three‑phase management framework of prevention, control, and remediation.
Facing massive data volumes and strict SLA requirements during the Double 11 shopping festival, a major e‑commerce platform built a systematic data‑governance framework that addresses quality, stability, cost, and efficiency through multi‑layered grading, digital cost models, automated tools, and full‑lifecycle management.
This article explains the legal and strategic importance of data classification and grading in China, outlines the relevant regulations, describes the principles and processes for implementing classification, and offers practical steps for enterprises to secure data while meeting compliance and business needs.
The first Data Governance Forum in Shanghai highlighted the intertwined challenges of data quality, compliance, and integration, emphasized the mutual reinforcement between large AI models and data governance, and presented perspectives from industry, academia, and legal experts on how to advance data as a strategic production factor.
This article outlines practical steps, challenges, and results of implementing data table compliance governance in a fast‑growing data warehouse, covering standards redefinition, decommissioning unused tables, metric reuse, ODS penetration reduction, and ongoing maintenance strategies.
This article outlines Bilibili's data quality assurance framework, detailing its evolution across four development stages, the current data platform architecture, identified pain points, four key quality objectives, core capabilities such as a quality data warehouse, comprehensive monitoring, digital optimization, fault handling, and future directions.
This article outlines how enterprises can enhance their big data governance capabilities during digital transformation, covering the background and challenges of data governance, the emergence of data capability as a core competency with implementation paths, and practical suggestions for governance projects, illustrated with national-level examples.
The article introduces Tencent's OLA data‑governance platform and its t‑Metric metric middle‑platform, explains the Headless BI concept, details the configuration‑driven metric production workflow, core capabilities, architecture, unified query service, ecosystem integration, and answers audience questions about real‑time analysis, dimension handling, and trust mechanisms.
This article explains what a data warehouse is, contrasts it with traditional databases, outlines the evolution from classic to internet‑scale warehouses, details modeling approaches and layered architectures, discusses KPI dictionaries, date dimensions, naming standards, data governance, incremental loading techniques, and upstream/downstream coordination.
The article presents Tencent Oula’s comprehensive data‑asset platform that integrates data collection, integration, warehouse and metric modeling, governance engines, and AI‑enabled analytics to reduce information entropy, standardize assets, and enable production‑as‑governance across the modern data stack.
The article explains the Chief Data Officer (CDO) role, its evolution, key responsibilities, compensation ranges, reporting structures, required skills, and how organizations across industries are adopting the position to drive data‑centric business value.
This article presents Alibaba's experience in addressing large‑scale data stability challenges by outlining common problems, governance principles, baseline monitoring, team collaboration methods, practical implementations, and proactive measures to ensure reliable and accurate data production on the DataWorks platform.
This article explains how to pinpoint the optimal data‑governance entry point at each phase of data‑warehouse development, using Meituan Delivery as a case study, and outlines long‑term strategies for technical standards, architecture, security, and platform implementation.
iQIYI’s data‑middle‑platform team built a four‑zone data lake—raw, product, work, and sensitive—integrated with unified ODS/DWD/MID layers, a metadata catalog, and self‑service tools, leveraging HDFS, Hive/Iceberg, Spark/Trino, and Flink, migrated to Apache Iceberg for real‑time freshness, and now aims to further streamline modules and adopt new technologies.
Large enterprises generate massive, diverse data across departments, leading to fragmented storage and trust issues, so this article outlines a comprehensive data governance implementation plan covering standards, metadata, quality, integration, asset, and security management to unify and secure enterprise data.
The article presents Volcano Engine DataLeap's comprehensive data‑governance framework for e‑commerce platforms, covering challenges of large‑scale warehouses, a top‑level governance architecture, systematic stability, cost, and tool efficiency systems, and detailed implementation steps to achieve autonomous, distributed governance.
This article details Taikang Life Insurance's end‑to‑end technical selection, architecture design, implementation, and custom enhancements of an Apache Hudi‑driven lakehouse platform for large‑scale health‑insurance data, covering background, component evaluation, performance benchmarking, multi‑layer architecture, and real‑world results.
This article presents a comprehensive case study of NetEase Cloud Music’s metadata‑driven intelligent governance platform, detailing its scale, construction background, modular architecture, rule‑based automation, practical deployment, and future roadmap for sustainable data ecosystem management.
The presentation details Ping An Life’s four‑stage data middle‑platform initiative—defining data capability as the foundation of digital transformation, outlining the platform’s architecture and governance, showcasing business‑value applications, and discussing talent and cultural considerations—to illustrate how a large insurer builds a scalable, real‑time data ecosystem.
This article outlines a comprehensive data governance framework for Chinese enterprises, covering organizational structures, data asset inventory, six‑stage methodology, and the creation of unified data standards and quality rules to support effective digital transformation and data‑driven decision making.
Data governance encompasses a comprehensive framework—including metadata, master data, standards, quality, assets, exchange, security, and lifecycle management—to ensure data’s accuracy, consistency, and value across an organization, offering step‑by‑step guidance, best‑practice models, and visual references for effective implementation.
The article details how the "Xingfu Li" real‑estate platform tackled a 13‑day offline data‑warehouse SLA delay by adopting Volcano Engine's DataLeap suite, outlining the challenges, the three‑step governance process, and the measurable improvements achieved across task coverage, alert reduction, and data stability.
This article provides a comprehensive guide to data metrics and tags, explaining their definitions, classifications, conversion methods, practical usage scenarios, and step‑by‑step approaches for building robust metric and tag systems that support strategic decision‑making and operational efficiency.
The article summarizes ByteDance's DataOps journey, detailing its mid‑platform tool and Data BP model, core performance metrics, quality, hardware and human efficiency challenges, concrete DataOps implementation, productization through DataLeap, best‑practice promotion, and future outlook for data‑driven business value.
This article presents Xiaomi's data‑driven governance framework, detailing a three‑level "rocket" plan, the asset health scoring model covering storage, compute, quality, security and compliance, productization efforts, future enhancements, and a Q&A session on implementation challenges.
This article analyzes the rise of lake‑house architecture in the Hadoop ecosystem, compares the technical capabilities of Hudi, Iceberg and Delta Lake, details implementation enhancements such as MOR and multi‑writer support, showcases Flink integration, presents a real‑time marketing use case, and outlines future development directions.
This guide reviews six popular Snowflake‑compatible tools—covering data preparation, visualization, integration/ETL, business intelligence, and governance—that can dramatically boost productivity for data professionals.
This article outlines a comprehensive methodology for delivering a data governance project, covering planning, blueprint design, implementation, and acceptance phases, with detailed guidance on team formation, stakeholder roles, requirement analysis, platform architecture, management processes, and post‑deployment operations.
This guide introduces the concept of a data mesh, explains why modern data‑driven organizations need domain‑driven self‑serve design, outlines its three core principles, and provides a curated reading list to help teams transition from monolithic data lakes to distributed, observable data products.
This article explains why fragmented data hampers business insight in large enterprises and provides a comprehensive overview of master data concepts, governance structures, standards, processes, and step‑by‑step implementation practices to achieve consistent, high‑quality enterprise data.
Facing fragmented metadata, unclear ownership, and costly data duplication, Weimeng implemented a comprehensive data asset governance framework—covering metadata standards, lineage visualization, metric normalization, and cost management—to boost data quality, security, and business value across its new‑retail platform.
This article explains what data assets are, why a data asset catalog is essential for data governance, and provides a step‑by‑step framework—including identification criteria, value dimensions, construction phases, tool support, and core functional modules—to help enterprises systematically create, manage, and leverage a data asset catalog.
This article explores the challenges of data quality in mature big‑data environments and presents intelligent, automated approaches—including assertions, automatic detection, rule recommendation, link checking, and collaborative mechanisms—to embed quality checks throughout the data pipeline, improving efficiency and reliability.
This article explains how metadata serves as essential infrastructure for data platforms, detailing Huawei's classification framework, governance challenges, management architecture, integrated modeling, data lake handling, service management, and data map construction to bridge business and IT domains.
This article explains what an indicator system is, how to design and analyze metrics, methods for managing them, and the right mindset for using indicator systems to improve business decision‑making and operational efficiency.
The article examines how high‑quality data, robust privacy protection, and blockchain‑enabled trust infrastructure are essential for unlocking the value of AI models, citing market forecasts, examples from smart‑car and fintech firms, and the growing Chinese big‑data market through 2026.
At the 2023 Big Data Industry Development Conference in Beijing, the China Communications Standards Association unveiled the top ten big‑data keywords, highlighting trends such as lake‑warehouse integration, data assetization, DataOps, intelligent analytics, data ethics, security, public data licensing, and cross‑border data flows.
This article outlines a complete data governance framework, detailing the five managerial domains—control, process, governance, technology, and value—along with strategies for data strategy, organizational structure, policies, processes, standards, quality, security, and platform tools, and highlights AI’s pivotal role in enhancing governance efficiency.
This article presents Bilibili's data service middle platform, detailing its background, one‑stop data service architecture, core processes, model and API construction, query mechanisms, full‑link control, cost‑reduction, high‑availability strategies, achieved results, and future roadmap.
This article presents iQIYI’s comprehensive data link governance practice, covering the motivations, offline and real‑time pipeline governance strategies, monitoring mechanisms, data lineage, and exploratory work such as intelligent attribution and field‑level lineage to improve data accuracy, timeliness, and reliability.
This article presents Big Data company's active data governance practice using operator-level lineage, detailing the shortcomings of traditional lineage, the implementation of indicator chain governance, and the exploration of proactive model governance to achieve smarter, more precise data management.
This article explains why tag scoring is essential for data governance, outlines a five‑dimensional scoring model—including usage, attention, quality, continuous optimization, and security—and demonstrates how the scores can drive dashboards, alerts, and resource‑saving decisions.
This article provides a comprehensive guide to planning, structuring, securing, and managing a data lake on Azure Data Lake Storage Gen2, covering zone architecture, folder hierarchy, access control, file formats, scalability considerations, and best‑practice recommendations for big‑data workloads.
This article presents a detailed case study of Huolala's big data security architecture, covering background challenges, lifecycle‑wide protection standards, data classification, encryption, disaster recovery, governance processes, and future improvement plans to enhance data asset protection and compliance.
This article details Huolala's practical experience in constructing a comprehensive big data security system, covering data lifecycle protection, classification standards, capability development, and governance, while balancing regulatory compliance and business growth.
This article proposes a digital portrait framework for data governance, detailing metrics for user experience across external customers, internal users, management, and technical staff, as well as architecture quality indicators covering model, distribution, standards, and assets.
This article examines the explosion of heterogeneous data sources, the limitations of traditional data lakes and warehouses, and proposes a distributed lakehouse architecture that integrates advanced management layers to improve data governance, reliability, and support both SQL and advanced analytics workloads.
This article explains how Ziroom implements a comprehensive data classification and grading system to meet the 2021 Data Security Law, improve risk management, optimize security resources, and boost user trust through automated tools, multi‑level categorization, and continuous manual verification.
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.
