The article argues that as AI models become commoditized, the decisive factor for enterprises is mastering data governance, data‑AI integration, and data flow, turning data into a strategic asset that creates a three‑layer moat and drives sustainable AI ROI.
The article outlines the evolution of big data in banking, compares management models for heterogeneous data, describes the shift from data engineering to knowledge engineering, introduces LLMOps for high‑quality knowledge bases, and details how integrating AI and data can enable a “one‑sentence bank” that answers queries and executes tasks.
The article recounts a real incident where an AI‑generated SOP cited outdated policy because a knowledge base was overloaded with unchecked historical documents, then outlines a step‑by‑step protocol—including corpus cleaning, version locking, and isolation zones—to prevent data contamination and ensure reliable AI outputs.
Lance File Format v2.2 tackles the AI data explosion by delivering hundred‑fold random‑read performance, advanced two‑layer compression, zero‑cost schema evolution, Git‑style versioning, external blob handling, and a roadmap toward native media support and intelligent encoding, positioning it as a core infrastructure for large‑scale multimodal workloads.
The article analyzes the three meanings of DRP, explains its role as a group‑level data‑driven control hub, contrasts it with ERP’s execution focus, debunks the myth that DRP will replace ERP, and outlines four practical obstacles—cognitive bias, data silos, organizational resistance, and talent shortage—along with concrete steps to ensure successful implementation.
The DACon conference in Shanghai gathered over 8,000 developers, managers and experts, delivering 50 talks that explored self‑evolving AI agents, data‑centric ontology, Agent‑Ready big‑data infrastructure, AI‑AR ecosystem evolution, and the emerging challenges of Agentic data governance.
A panel of industry experts dissected Palantir’s rapid growth, revealing that its advantage lies in a systematic ontology‑driven methodology rather than exclusive technology, and argued that Chinese firms can adopt the same approach if they first resolve data governance, semantic consistency, and management challenges.
The DACon conference in Shanghai gathered over 8,000 developers and experts, showcasing 50 talks that explored self‑evolving AI agents, the open‑source GenericAgent framework, data‑governance ontology, Agent‑Ready big‑data infrastructure, and AI+AR ecosystems, while highlighting practical case studies and future industry directions.
The report analyzes why AI project failure rates remain 70‑85%, how data‑application illusion and workslop erode productivity, and why integrating Agentic AI into native workflows is the only viable path, while highlighting a 16% drop in Gen Z AI‑related job placements and practical mitigation strategies.
Tencent Games’ data governance team details an AI‑driven, end‑to‑end semantic framework that shifts traditional rule‑based data management to a service‑oriented model, cutting storage waste by 30 %, halving development time, and boosting asset recommendation accuracy to 95 % across its global gaming platform.
The article analyzes how AskTable uses AI agents to replace static BI dashboards with an immersive, real‑time data‑analysis canvas, enabling business users to query multiple data sources in seconds, while addressing accuracy, table‑finding, and fine‑grained permission challenges.
The article argues that the failure of many enterprises to benefit from AI is not due to a lack of technology but to insufficient digital foundations, disorganized processes, poor data quality, cultural resistance, and a shortage of skilled talent, turning AI projects into costly showpieces.
The article analyzes why natural‑language database queries often fail, outlines four technical routes, presents a five‑layer architecture with a business‑semantic middle layer, shares engineering best practices, a real‑world case study, and a product comparison to guide data companies in designing an effective intelligent query system.
This talk details how Tencent Games leverages AI to transform its data governance from rule‑based, passive processes into a semantic, service‑oriented paradigm, addressing resource waste, low collaboration efficiency, and scalability challenges while delivering measurable improvements in cost, speed, and asset quality.
The article analyzes how leading internet companies embed AI agents across the entire data‑warehouse lifecycle to automate governance, presenting real‑world case studies from Alibaba, ByteDance, JD.com and Tencent, and quantifies benefits such as over 65% reduction in manual effort, 50% drop in metric duplication, and a 40% boost in resource utilization.
In the AI era, data consumption moves from a few technical users to all business staff, forcing a fundamental redesign of data governance across five dimensions—resource consumption, frequency, semantics, knowledge base, and modality—and proposing three actionable strategies to make data semantically rich, fully multimodal, and AI‑consumable.
The article outlines four AI‑driven breakthroughs reshaping data work—AI‑for‑Data automation, generative‑AI‑enhanced governance, NoETL real‑time lake ingestion, and next‑generation SQL analysis—detailing their problems, concrete case studies, implementation steps, pitfalls, and measurable efficiency gains.
The live discussion breaks down the practical challenges of building enterprise‑grade Data Agents—from unified semantic layers and prompt engineering versus model fine‑tuning, to table discovery, multi‑turn memory, trust, cost control, and continuous improvement—showing why real‑world AI success hinges on system reliability rather than raw model power.
A closed‑door forum gathered experts from academia and leading Chinese tech firms to dissect Palantir’s ontology‑driven approach, comparing it with conventional data modeling, exploring AI integration, and highlighting the managerial and technical challenges that determine its success in enterprise environments.
This article analyzes how large language models can be integrated into data governance workflows, outlines three practical use cases, identifies five common implementation traps, offers best‑practice recommendations, and presents a real hospital case that demonstrates measurable performance gains.
The article analyzes common pitfalls of CMDB implementations, explains why overly comprehensive models collapse, and proposes a consumption‑driven, federated, and automation‑focused approach that integrates monitoring, ITSM, and FinOps to achieve continuous data quality and business value.
Most companies see data governance fail not because of technology but because they start with the wrong direction, focusing on rules, platforms, and processes that add friction instead of improving data usability, and the article provides a step‑by‑step, low‑overhead approach with concrete SQL and Python templates to fix it.
This article compiles a comprehensive set of data‑warehouse interview questions—including self‑introduction prompts, SQL and window‑function challenges, data‑skew solutions, architecture design, file‑format trade‑offs, governance, and team‑leadership topics—to help candidates prepare for first, second, and third‑round interviews at leading tech firms.
In the era of AI and multi‑cloud, this article analyzes the core challenges of data governance—data silos, quality gaps, and compliance risks—and explains how Apache Gravitino’s unified metadata architecture together with OpenLineage’s standardized lineage model provide a scalable, automated solution for intelligent, real‑time data management.
The article examines why most companies are still at the proof‑of‑concept stage for AI‑enabled testing, outlines three practical pillars—data, scenario selection, and closed‑loop feedback—and warns of common pseudo‑AI pitfalls through concrete industry case studies.
In this interview, senior technology specialist Chen Ke shares how he adapts internet‑scale microservice and PaaS practices to the highly regulated financial sector, discusses building enterprise knowledge‑base platforms with large language models, outlines data‑governance and compliance strategies, and predicts the evolving skill set engineers will need.
The article explains how the newly released "AI Training Data Set Delivery and Quality Acceptance Specification" addresses gaps in existing data‑quality standards by defining a three‑layer acceptance framework, quantitative metrics, and a pre‑negotiated quality‑baseline mechanism to make dataset delivery verifiable and directly supportive of model training goals.
UltraData presents a five‑level tiered data‑management system (L0‑L4) for large‑language‑model training, releases the world’s largest open L3 mathematics dataset (2.4 TB), validates the approach with extensive MiniCPM‑1.2B experiments showing consistent performance gains across web, multilingual, math and code domains, and opens a suite of governance tools and a community portal.
A well‑defined naming convention for data‑warehouse tables reduces chaos, improves maintainability, speeds up queries, and cuts cross‑team collaboration costs, turning raw data into a strategic asset for modern enterprises.
The article analyzes AI testing platforms, showing how automated test‑case generation, adaptive execution, defect prediction, and a structured rollout process deliver up to 35% higher coverage, 48% faster design, and 40% reduced execution time across finance and e‑commerce case studies.
This article explains how dbt drives automated data modeling and governance, how DataOps practices bring agility and control to data projects, and how StarRocks’ lakehouse architecture enables real‑time and batch analytics, illustrated with concrete workflows, version‑control conventions, and enterprise case studies.
The article walks through the evolution of data management—from idealistic visions and messy “shit mountains” to the realities of data lakes, metadata layers, governance challenges, trust breakdowns, and finally the promise of DataOps as a hopeful path forward.
This article details how a Chinese e‑commerce platform leverages large‑language‑model AI, the open‑source Dify platform, and engineered workflows to automate security tagging of massive data assets, covering data‑governance fundamentals, AI‑driven tagging advantages, technical architecture, prompt engineering, optimization cases, and future roadmap.
This comprehensive guide walks you through fundamental database design goals, a step‑by‑step lifecycle, nine essential strategies—including normalization, indexing, and security—plus modern distributed and NoSQL considerations, performance tuning, high‑availability tactics, and practical tools for robust data governance.
This article explains how AI agents transform traditional data governance by introducing a four‑layer perception‑decision‑execution‑learning architecture, detailing the required technologies, tool integrations, code examples, deployment steps, team roles, security safeguards, and practical rollout strategies for enterprises seeking automated, intelligent data management.
A detailed transcript of a closed‑door forum reveals how Palantir’s ontology methodology, combined with AI agents, addresses data semantics, knowledge governance, and enterprise‑level decision making, while highlighting practical challenges, evaluation frameworks, and the need for strong management and high‑quality data foundations.
This article shares the author's practical experience in using AI to tackle complex repetitive tasks, presenting a reusable methodology that abstracts human actions into a perception‑decision‑execution loop, and demonstrates three automation modes—tool assistant, workflow, and intelligent agent—through real‑world cases in data governance, ticket handling, and baseline operations.
This article details Baidu's MEG data cost governance practice, covering background challenges, a unified governance framework, health‑score metrics, platform and engine capabilities, concrete compute and storage optimization techniques, achieved results, and future plans for continuous cost reduction.
The 2025 Open Atom Developer Conference highlighted JD's JoyAgent project, an open‑source, 100% enterprise‑grade multi‑agent platform that excels in AI, data governance, and diagnostic analysis, with detailed features, performance metrics, and deployment experiences shared.
This article examines the challenges of traditional banking data, explains how large language models can fuse structured and unstructured information, outlines a new data‑centric infrastructure and governance approach, and describes the DiFY platform’s AI‑agent and DataOps capabilities for agile, non‑intrusive integration with core banking systems.
The article explains how the rapid rise of large‑model AI has shifted the focus from models to data, outlines the concept and stages of AI‑specific data governance, identifies challenges such as low‑quality data, privacy leaks, bias, and proposes a comprehensive framework of principles, processes, and technologies to ensure high‑quality, secure, and ethical AI deployment.
The article explains how inconsistent "customer ID" fields across systems stem from a lack of unified data models, defines the difference between data modeling and data models, outlines three modeling stages, and compares three major modeling approaches—normative, dimensional, and entity—highlighting their purposes, processes, and trade‑offs.
This article explores the evolution, current state, and challenges of enterprise unstructured data management, reviews case studies from traditional firms, Huawei and Ant Group, proposes an ECM‑based reference framework, compares it with structured data governance, and outlines future integration strategies with AI and unified data platforms.
This article explores Kuaishou E‑Commerce’s journey in constructing a comprehensive data metric system, detailing its business context, the necessity of metrics, challenges across data production, querying and usage, practical implementation steps, management practices, and a concluding Q&A.
This article analyzes why analysts and data engineers clash over non‑reusable data models, presents metrics such as cross‑layer reference rate and model reuse coefficient, and outlines a step‑by‑step framework—including ODS takeover, subject‑domain mapping, dimension consistency, fact‑table integration, development best practices, and tool support—to transform siloed warehouses into a shared data‑platform.
This article shares Ant Group’s practical experience in large-scale data governance, outlining four main topics—overall governance overview, data quality management, data storage-processing governance, and future considerations—while emphasizing the five critical aspects of architecture, security, compliance, quality, and value that drive effective big-data operations.
This article introduces Douyin Group’s one‑stop Data Asset Management Platform, explains why the company focuses on data assets rather than raw metadata, and details the evolution, architecture, applications, and future outlook of its comprehensive big‑data lineage system.
This article introduces Douyin Group’s Data Asset Management Platform, explaining its shift from traditional metadata to comprehensive data assets, detailing the evolution, architecture, and applications of its full‑link big data lineage, and offering strategic guidance for building effective lineage systems.
This article shares a data‑governance roadmap for small and medium banks, covering industry pain points, high‑quality data sets, a three‑step governance path, data standards, metadata management, master‑data strategy, business data modeling, a hybrid Greenplum‑Hadoop platform, quality monitoring, and a maturity assessment framework.
Ant Group shares its practical experience in big data governance, outlining five key focus areas—architecture, security, compliance, quality, and value—through four structured sections and detailed discussions on data quality and storage governance, while also exploring future challenges and the economics of data.
This article explores AI governance and ethics, outlining five key parts: AI business scenarios, data and AI risks, a comprehensive governance framework, practical implementation steps, and measurable benefits, while also providing expert insights and a Q&A session for deeper understanding.
This article explains Alibaba Cloud's Log Service (SLS) soft‑delete feature, describing its mark‑and‑filter mechanism, implementation steps, and real‑world scenarios where it replaces costly hard‑delete or ETL solutions with near‑instant, low‑impact data removal for compliance, emergencies, and test‑data contamination.
This article explains how the natural entropy of massive data sets creates governance challenges, outlines four core obstacles faced by large internet companies, and presents a sustainable, metric‑driven framework—including quality measurement, indicator systems, and future‑oriented operations—to achieve orderly data asset management.
This article explains how SQL lineage (bloodline) technology can be leveraged in offline data warehouses to enable precise data discovery, automated tag propagation, fine‑grained data governance, column‑level TTL management, and dynamic masking for data protection, illustrating implementation steps, strategies, and real‑world use cases.
This article presents a curated catalog of cutting‑edge topics covering financial large‑model data governance, proactive metadata systems, data cleaning and compliance technologies, AI‑driven intelligent operations, and generative data analysis solutions, inviting readers to explore the latest AI innovations.
This article presents a curated catalog of cutting‑edge topics covering AI‑powered data governance, large‑model applications, data cleaning, compliance, lakehouse integration, intelligent operations, and generative analytics, inviting readers to explore the latest innovations and download the full e‑book via QR code.
This article presents a comprehensive catalog of cutting‑edge AI and large‑model topics, covering financial data governance, proactive metadata systems, data cleaning compliance, lake‑warehouse integration, intelligent operations, generative analytics, and QR‑code access to the full e‑book.
This article details Baidu MEG’s TDS (Turing Data Studio) platform, explaining its three‑pillar governance framework—process standardization, quality controllability, and intelligent operations—along with concrete mechanisms, automation, and measurable results that dramatically improve data reliability, operational efficiency, and fault‑tolerance in large‑scale data production.
JD Retail outlines the growing data management challenges it faces—including asset discovery, architecture agility, development quality, and rising IT costs—and presents a comprehensive data governance framework that leverages standards, agile architecture, development isolation, and resource optimization to improve efficiency and reduce operational expenses.
JD Retail faces growing data volume, redundant models, and resource‑intensive storage, prompting a comprehensive data‑governance strategy that defines standards, streamlines architecture, isolates development, and optimizes compute and storage costs, ultimately enabling more efficient, secure, and agile data operations across the enterprise.
JD Retail confronts growing data volume, redundant models, shared account risks, and rising storage costs, and responds with a comprehensive data governance framework that standardizes data, streamlines architecture, isolates development, and optimizes resources to achieve efficient, secure, and cost‑effective data operations.
A comprehensive list of interview topics covering self‑introduction, career moves, data‑warehouse design, team building, architecture comparisons, fact‑table classification, common dimensions, performance tuning, and data‑governance for aspiring big‑data engineers.
This report analyzes how treating data as a production factor reshapes manufacturing, outlines three major challenges—scenario explosion, business‑application enrichment, and intelligent‑application expansion—and shares concrete governance, platform, and AI‑model practices that enable agile, data‑driven digital transformation.
This article introduces the comprehensive design and evolution of a unified metadata lineage platform for big data, covering background, data processing chain, lineage models, system architecture, quality metrics, application scenarios, and future plans to enhance data governance, quality, and security.
This article presents a curated list of case studies and insights on how Lakehouse technology powers real‑time analytics, simplifies data architecture, and drives digital transformation across industries such as e‑commerce, automotive, and social media.
This article shares a collection of PPT slides that showcase classic data architecture and data model design methods, offering valuable insights for building data middle platforms and master‑data‑driven governance solutions.
This article compiles a series of expert case studies and insights on real‑time intelligent fully‑managed Lakehouse technology, illustrating how companies such as SalesEasy, Chang’an Auto, Kuaishou, Tencent, and JD.com leverage lakehouse architectures to achieve advanced multi‑dimensional analytics, cost‑performance balance, and effective data governance in the digital economy.
This guide presents a curated collection of case studies and insights on cloud-native Lakehouse architectures, real‑time analytics, data‑driven user experiences, and data governance, showcasing implementations from companies like SalesEasy, Changan Auto, TikTok, Tencent, JD.com, and more.
This article presents a curated list of sessions covering cloud Lakehouse technology for real-time, multidimensional data analysis, including case studies from SalesEasy, Changan Auto, Tencent, and JD, as well as discussions on data lake adoption, streaming lake Paimon, and the relevance of metadata‑driven data governance in the digital economy.
This article presents a curated list of case studies and insights on cloud Lakehouse technology, covering real-time intelligent analytics, data architecture simplification, IoT big‑data platforms, integrated data platforms, and the evolving role of metadata‑driven data governance in the digital economy.
Enterprises rushing to purchase DeepSeek AI appliances and smart‑agent platforms often face hidden technical, data, and organizational challenges that turn promised "plug‑and‑play" solutions into costly missteps, highlighting the need for realistic strategy, robust data governance, and continuous capability building.
AI-era data architecture must handle massive, multimodal datasets with real-time processing, prioritize data quality over quantity, support scalability, provenance, and native ML/AI integration, while addressing governance, security, and ethical challenges through emerging technologies like data fabric, mesh, and federated learning.
This article details Unicom Digital’s metadata management practice on its integrated data platform, covering the strategic background of data, key challenges, award-winning capabilities, three-pronged solutions—automation, linking+, and AI—along with practical implementations, full‑chain lineage, data responsibility, lifecycle management, and future AI‑driven enhancements.
The article explains why high‑quality, diverse data—rather than just advanced models—has become the decisive factor for enterprise AI success, outlining key dimensions of data quality, strategies for building diverse datasets, and practical steps for establishing a data‑first AI strategy.
WeddingJoy, a comprehensive wedding service platform, transformed its data infrastructure by adopting Alibaba Cloud DataWorks, addressing challenges of massive heterogeneous data, improving data governance, enabling AI-driven insights, and boosting development efficiency across its ecosystem.
The article explains why establishing a unified data model is essential, differentiates data modeling from data models, outlines three modeling stages, compares normative, dimensional, and entity modeling methods, and provides practical steps and diagrams to help organizations build robust, business‑driven data architectures.
Apache Gravitino is an open‑source metadata service platform that provides a unified, high‑performance, geographically distributed metadata lake, enabling end‑to‑end data governance, multi‑engine access, and direct management of both structured and unstructured data assets across diverse systems.
This article outlines a three‑stage "治理‑理‑AI" model for data governance, detailing top‑level architecture, management capabilities, AI‑driven automation, compliance measures, and a practical implementation roadmap to maximize data asset value in the digital economy.
The article outlines a comprehensive live‑streaming ecosystem governance framework that combines data‑mining engines, tagging platforms, rule‑based disposal mechanisms, and multi‑stage user touchpoints to improve content quality, compliance, and platform sustainability.
This article introduces AZ Mover, a lightweight HDFS client‑side tool that intelligently scans, schedules, and migrates block replicas across multiple availability zones, detailing its design goals, core workflow, command‑line options, concurrency controls, and future enhancements for robust big‑data disaster recovery.
The article reports Xiaohongshu’s cancellation of the big‑small week schedule and non‑compete clause, then provides a collection of big‑data interview questions—including data governance, Iceberg metadata management, and lakehouse production best practices—along with concise answers and resources for candidates.
This article explains the core concepts of metadata, master data, data resources, data governance, and data management, outlines their roles, compares governance with management, and provides practical steps and best‑practice recommendations for building a robust enterprise data framework.
This guide outlines a comprehensive data development roadmap, covering infrastructure setup, governance frameworks, automated pipelines, BI and analytics tools, AI/ML integration, cultural adoption, and continuous performance monitoring to enable intelligent business transformation.
This article examines how Retrieval‑Augmented Generation (RAG) and multi‑agent technologies narrow the gap between large language models and practical deployment, highlighting their roles in operations automation, financial risk control, intelligent data governance, database localization, edge inference, and future AI‑driven solutions.
This article provides a comprehensive data‑warehouse self‑assessment checklist covering six core modules—data quality, security, compliance, process management, technology tools, and personnel training—with over 200 detailed inspection items to help organizations evaluate and improve their data governance practices.
This article examines the current state and future prospects of large AI models and inference acceleration, covering technology trends, diverse application scenarios from research to industry, and the challenges and opportunities that lie ahead for intelligent data governance, multimodal agents, and AGI.
The article emphasizes that while AI can assist data governance, organizations must first build a solid, high‑quality data asset knowledge base through sustained effort, otherwise they will face the inevitable problem of garbage‑in‑garbage‑out.
This report outlines a comprehensive framework for turning raw data into valuable assets, introducing DataOps and panoramic data architecture, and detailing practical methods for data value assessment, asset circulation, and operational mechanisms to help enterprises build a solid value baseline and expand data asset applications.
This article offers a comprehensive, multi‑angle guide to building and applying data indicator systems, covering core principles, step‑by‑step construction flow, data collection, cleaning, validation, and continuous optimization to empower digital transformation and high‑quality business growth.
The DataFun Knowledge Map 3.0’s graph database module, presented by Ant Group expert Cui Anqi, outlines how graph databases enhance complex analysis through risk‑control architectures, user‑relationship recommendation, data‑governance, a new graph‑based data management system, and the GraphRAG framework, while also offering a free download link.
This article introduces Douyin Group’s end‑to‑end data asset management platform, explains the evolution and architecture of its large‑scale data lineage system, presents quality metrics and ecosystem components, and outlines practical applications and future directions for data governance, development, and security.
This article explains why data is a new production factor, introduces the “three elements” (organization & awareness, processes & standards, platforms & tools) and the “four‑movement” (inventory, assessment, governance, sharing) framework for data asset operation, and shows how it drives digital transformation, efficiency and innovative business models.
This article analyzes JD Retail's approach to automating AB experiment workflows by introducing a data‑weaving framework that unifies metric definitions, streamlines logical data modeling, and enables scalable, real‑time DAG orchestration across multiple experiment scenarios.
This article shares the foreign bank's big data implementation journey, covering background and goals, overall planning and architecture, practical insights, phased rollout, data governance, security, and Q&A, illustrating how a unified data platform, storage‑compute separation, and AI‑driven tools drive business innovation.
This article compiles the official definitions released by China’s National Data Bureau and other agencies for 41 data‑related terms, explains the concepts of data infrastructure, privacy‑preserving computing, trusted data spaces, and blockchain, and outlines how these definitions guide the nation’s data‑driven development strategy.
JD.com’s data‑governance framework combines a health‑score‑driven, automated platform that cross‑verifies audit logs, builds full‑link and operator‑level lineage, introduces standard fields, and optimizes resource mixing, task staggering, and cross‑datacenter scheduling, while targeting real‑time AI‑enhanced detection and full automation.
This article explains how data can empower the digital transformation of traditional manufacturing, covering background policies, challenges in building industrial data indicator systems, overall architecture design, technical and business considerations, and practical solutions such as the 4+4 principle, KPI loops, and case studies.
This article presents the motivation, design principles, architecture, metric system, and results of a data‑warehouse evaluation framework that quantifies efficiency, quality, cost, and model health to drive systematic governance and continuous improvement across the organization.
The article explains how recent policies make data asset inclusion in financial statements essential, outlines the concepts of data resources, assets and factors, describes the governance, assessment and lifecycle processes, and shows how this practice can boost financing, valuation and digital transformation for companies, economies and nations.
This article presents JD.com’s comprehensive big‑data governance experience, detailing the background and challenges, the automated governance platform and its core technologies such as audit logs and full‑link lineage, strategies for resource optimization, and the roadmap toward real‑time, intelligent, and fully automated data governance.
