[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128967-en":3,"doc-seo-128967-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128967,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Designing the AI-Driven Data Foundations - Architecture, Principles, and Practice","Designing the AI-Driven Data Foundations explores how generative AI reshapes data system design, operations, and professional roles. It lays out an AI-driven data foundation across operational and analytical stores, evaluates platforms for AI workloads, and outlines data engineering approaches using AI agents. The book addresses governance, data trust through quality/security/privacy/compliance, and operational practices such as observability and DataFinOps. It also covers data architecture for generative AI via AI-ready and synthetic data, RAG, and retrieval-driven agent workflows.","Table of Contents  \nCover  \nTable of Contents  \nTitle Page  \nPreface  \nIntroduction  \nWhy This Book  \nWhat Sets This Book Apart  \nWho This Book Is For  \nWhat You Will Learn  \nCHAPTER 1: Charting the AI-Driven Data Foundation  \nMarket Dynamics Fueling a Rethink of Data Foundations  \nReasons for an AI-Driven Data Foundation  \nOpportunities to Modernize Data Foundations  \nChallenges Facing Data Foundations  \nGuiding Principles for a Unified Data and AI Strategy  \nBuilding Blocks of an AI-Driven Data Foundation: Outline of  \nThis Book  \nConclusion  \nCHAPTER 2: Operational Data Stores  \nUnderstanding Data Types  \nThe Evolution of Data Stores  \nData Store Taxonomy  \nTrends in Operational Data Stores  \nConclusion  \nCHAPTER 3: Analytical Data Stores  \nTypes of Analytical Data Stores  \nArchitectural Trends  \nAnalytical Query Engine Mechanics  \nOpen-Source Analytics Foundations  \nConclusion  \nCHAPTER 4: Evaluating Data Stores for AI Workloads  \nEvolution of the Data Space  \nFramework for Evaluating and Selecting Data Stores  \nUse Cases and Data Model  \nArchitecture  \nAvailability  \nElastic Scalability  \nPerformance  \nData Access and User Experience  \nGovernance and Security  \nOperations, Migration, and Administration  \nVendor and Pricing Considerations  \nConclusion  \nCHAPTER 5: AI-Driven Data Engineering  \nRequirements of AI-Driven Data Engineering  \nData Engineering Approaches  \nData Engineering Agents  \nDecision Framework for Choosing Integration Patterns  \nConclusion  \nCHAPTER 6: Convergence of Analytics, AI, and Data Products Analytical Approaches  \nAnalytics Technologies  \nSemantic Layers and Organizational Context  \nData Products  \nAnalytics in the Decision Flow  \nConclusion  \nCHAPTER 7: Data Architecture for Generative AI  \nAI-Ready Data  \nSynthetic Data  \nRetrieval-Augmented Generation  \nAI Agents  \nConvergence of GenAI and Databases  \nImplications for Data Professionals  \nConclusion  \nCHAPTER 8: Data and AI Governance Framework  \nWhy Governance Fails  \nMetadata  \nData and AI Governance Framework  \nConclusion  \nCHAPTER 9: Data and AI Trust: Quality, Security, Privacy, and Compliance  \nData Quality for AI  \nData Security Foundations  \nPrivacy Protection  \nCompliance Frameworks  \nSecuring the Data Foundation for AI Workloads  \nBuilding a Unified Trust Architecture  \nConclusion  \nCHAPTER 10: Operations for the AI-Driven Data Foundation  \nThe Evolution of Data Operations  \nHow Agent Workloads Change Operations  \nOperating on Evolving Data  \nObservability for Data and AI Systems  \nDataFinOps for Data and AI Workloads  \nAI Agents for Operations  \nConclusion  \nAcknowledgments  \nAbout the Author  \nAbout the Technical Editor  \nIndex  \nCopyright  \nDedication  \nEnd User License Agreement  \nList of Tables  \nChapter 1  \nTable 1-1: Key Roles of an AI-Driven Data Foundation  \nTable 1-2: Guiding Principles for an AI-Driven Data Foundation  \nChapter 5  \nTable 5-1: Traditional Data Engineering Compared to AI Agent  \nCapabilities  \nChapter 6  \nTable 6-1: Analytical Approaches Mapped to Organizational  \nPurpose  \nList of Illustrations  \nChapter 1  \nFigure 1-1: Layered architecture of an AI-driven data foundation:  \ningestion an...  \nChapter 2  \nFigure 2-1: The Cambrian explosion of data stores. Since the  \nearly 2000s, the ...  \nChapter 4  \nFigure 4-1: This diagram captures directional industry trends, not prescriptiv...  \nFigure 4-2: Strategic evaluation criteria spanning technical  \ncapabilities (arc...  \nChapter 5  \nFigure 5-1: The spectrum of data integration patterns, from  \nphysical movement ...  \nChapter 6  \nFigure 6-1: The analytics architecture stack. Data flows upward  \nfrom source sy...  \nChapter 7  \nFigure 7-1: The generative AI data stack. Context connects each layer, from da...  \nChapter 8  \nFigure 8-1: Comprehensive data and AI governance framework  \nChapter 10  \nFigure 10-1: The cyclical nature of DataOps showing continuous iteration acros...  \n[OceanofPDF.com](OceanofPDF.com)  \nDesigning the AI-Driven Data Foundations  \nArchitectur","cbCaimw9sXUan85P","https://ap.wps.com/l/cbCaimw9sXUan85P","pdf",3284180,11,1,367,"English","en",105,"# Preface\n# Introduction\n## Why This Book\n## Who This Book Is For\n# Chapter 1: Charting the AI-Driven Data Foundation\n# Chapter 2: Operational Data Stores\n# Chapter 3: Analytical Data Stores\n# Chapter 4: Evaluating Data Stores for AI Workloads\n# Chapter 5: AI-Driven Data Engineering\n# Chapter 6: Convergence of Analytics, AI, and Data Products\n# Chapter 7: Data Architecture for Generative AI\n# Chapter 8: Data and AI Governance Framework\n# Chapter 9: Data and AI Trust\n# Chapter 10: Operations for the AI-Driven Data Foundation\n# Acknowledgments\n# Index","[{\"question\":\"What makes an AI-driven data foundation different from traditional data foundations?\",\"answer\":\"The book frames generative AI as a force that changes both architecture and day-to-day work. It emphasizes that agents will increasingly consume data, requiring a foundation designed for AI workloads and operations.\"},{\"question\":\"How does the book structure AI-ready data systems?\",\"answer\":\"It starts from operational data stores and analytical data stores, then introduces evaluation frameworks for AI workloads. It continues with data engineering using AI agents and an architecture for generative AI that includes AI-ready data, synthetic data, and retrieval-augmented generation.\"},{\"question\":\"Which governance and trust topics are covered for data used by AI?\",\"answer\":\"It explains why governance fails, then presents a governance framework centered on metadata. It also covers data quality, security, privacy protection, compliance frameworks, and a unified trust architecture for AI workloads.\"}]","Designing the AI-Driven Data Foundations - Architecture, Principles, and Practice | PDF",1786006760,925,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"designing-the-ai-driven-data-foundations-architecture-principles-and-practice","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/technology/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/designing-the-ai-driven-data-foundations-architecture-principles-and-practice/128967/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-29","2026-08-06",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What makes an AI-driven data foundation different from traditional data foundations?","Question",{"text":77,"@type":78},"The book frames generative AI as a force that changes both architecture and day-to-day work. It emphasizes that agents will increasingly consume data, requiring a foundation designed for AI workloads and operations.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the book structure AI-ready data systems?",{"text":82,"@type":78},"It starts from operational data stores and analytical data stores, then introduces evaluation frameworks for AI workloads. It continues with data engineering using AI agents and an architecture for generative AI that includes AI-ready data, synthetic data, and retrieval-augmented generation.",{"name":84,"@type":75,"acceptedAnswer":85},"Which governance and trust topics are covered for data used by AI?",{"text":86,"@type":78},"It explains why governance fails, then presents a governance framework centered on metadata. It also covers data quality, security, privacy protection, compliance frameworks, and a unified trust architecture for AI workloads.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,115,120,125,130,133,137],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":113,"slug":114},50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":123,"slug":124},8,"Research & Report",30,"research-report",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":128,"slug":129},9,"Religion & Spirituality",20,"religion-spirituality",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":128,"slug":132},"World Cup","world-cup",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":134,"slug":136},10,"Lifestyle","lifestyle",{"id":138,"doc_module":4,"doc_module_name":47,"category_name":139,"show_sort_weight":108,"slug":140},19,"General","general"]