Data Fabric and Data Mesh Approaches with AI: A Guide to Ai-Based Data Cataloging, Governance, Integration, Orchestration, and Consumption (Paperback)

Understand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance--all designed to deliver "data as a product" within hybrid cloud landscapes.

This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive overview on how a data fabric architecture uses artificial intelligence (AI) and machine learning (ML) for automated metadata management and self-service data discovery and consumption. You will learn how data fabric and data mesh relate to other concepts such as data DataOps, MLOps, AIDevOps, and more. Many examples are included to demonstrate how to modernize the consumption of data to enable a shopping-for-data (data as a product) experience.

By the end of this book, you will understand the data fabric concept and architecture as it relates to themes such as automated unified data governance and compliance, enterprise information architecture, AI and hybrid cloud landscapes, and intelligent cataloging and metadata management.

What You Will Learn

• Discover best practices and methods to successfully implement a data fabric architecture and data mesh solution
• Understand key data fabric capabilities, e.g., self-service data discovery, intelligent data integration techniques, intelligent cataloging and metadata management, and trustworthy AI
• Recognize the importance of data fabric to accelerate digital transformation and democratize data access
• Dive into important data fabric topics, addressing current data fabric challenges
• Conceive data fabric and data mesh concepts holistically within an enterprise context
• Become acquainted with the business benefits of data fabric and data mesh

Who This Book Is For
Anyone who is interested in deploying modern data fabric architectures and data mesh solutions within an enterprise, including IT and business leaders, data governance and data office professionals, data stewards and engineers, data scientists, and information and data architects. Readers should have a basic understanding of enterprise information architecture.

了解現代數據織物和數據網格概念，利用基於人工智能的自助數據發現和交付能力，一系列智能數據集成風格，以及自動統一數據治理，旨在在混合雲環境中提供“數據作為產品”。

本書教你如何成功部署最先進的數據網格解決方案，並全面了解數據織物架構如何使用人工智能（AI）和機器學習（ML）進行自動元數據管理和自助數據發現和消費。你將學習數據織物和數據網格與其他概念（如數據運營、機器學習運營、AI開發運營等）的關聯。書中包含了許多示例，以演示如何現代化數據消費，實現購物數據（數據作為產品）體驗。

通過閱讀本書，你將了解數據織物概念和架構，以及與自動統一數據治理和合規性、企業信息架構、人工智能和混合雲環境以及智能目錄和元數據管理等主題的關聯。

你將學到什麼：
- 發現成功實施數據織物架構和數據網格解決方案的最佳實踐和方法
- 了解關鍵的數據織物能力，例如自助數據發現、智能數據集成技術、智能目錄和元數據管理以及可信AI
- 認識數據織物對於加速數字化轉型和民主化數據訪問的重要性
- 深入探討重要的數據織物主題，解決當前數據織物挑戰
- 在企業上下文中全面理解數據織物和數據網格概念
- 熟悉數據織物和數據網格的業務益處

適合閱讀對象：
對於在企業中部署現代數據織物架構和數據網格解決方案感興趣的任何人，包括IT和業務領導者、數據治理和數據辦公室專業人員、數據管理員和工程師、數據科學家以及信息和數據架構師。讀者應具備基本的企業信息架構的理解。

Eberhard Hechler is an Executive Architect at the IBM Germany R&D Lab. He is a member of the Data and AI development organization and addresses the broader analytics scope, including machine learning (ML). After more than two years at the IBM Kingston Lab in New York, he worked in software development, performance optimization, IT/solution architecture and design, Hadoop and Spark integration, and mobile device management (MDM).

Eberhard worked with Db2 on the MVS platform, focusing on testing and performance measurements. He has worked worldwide with IBM clients from various industries on a vast number of topics such as data and AI, information architectures, and industry solutions. From 2011 to 2014, he was at IBM Singapore, working as the Lead Big Data Architect in the Communications Sector of IBM's Software Group throughout the Asia-Pacific region.

Eberhard has studied in Germany and France, and holds a master's degree (Dipl.-Math.) in Pure Mathematics and a bachelor's degree (Dipl.-Ing. (FH)) in Electrical Engineering. He is a member of the IBM Academy of Technology, and has co-authored the following books:: Enterprise MDM, The Art of Enterprise Information Architecture, Beyond Big Data, and Deploying AI in the Enterprise (Apress).

Maryela Weihrauch is an IBM Distinguished Engineer in the Data and AI development group for IBM Z Technical Sales, and is a Customer Success leader. She has extensive experience with relational databases in terms of systems, application, and database design. She is engaged with enterprises across the world and helps them adopt new data and analytics technologies. Her former roles in Db2 for z/OS development have involved determining a Db2 for z/OS strategy for HTAP (Hybrid Transaction and Analytics Processing), including the Db2 Analytics Accelerator strategy and implementation as well as Db2's application enablement strategy.

Maryela consults with enterprises around the globe on many data modernization initiatives and leads an effort to develop a methodology to determine the best data architecture for a given application based on data architecture decision criteria.

Maryela holds two master's degrees in Computer Science from Technical University Chemnitz, Germany and California State University, Chico, California, USA. She holds a number of patents and is a member of the IBM Academy of Technology. She frequently shares her experience at conferences around the world.

Yan (Catherine) Wu is the Program Director at the IBM Silicon Valley Lab. She is an engineering leader with deep expertise in data governance, artificial intelligence (AI), machine learning (ML), enterprise design thinking, and pragmatic product marketing. She has extensive experience working with large clients to discover use cases for data governance and AI, explore how the latest technologies can be applied to resolve real-world business challenges, and deploy these technologies to accelerate enterprise digital transformation. She has a proven track record in translating customer needs into software solutions while working collaboratively with globally distributed development, design, and offering management teams.

Prior to her current position at IBM US, Catherine was the Lab Director of the Data and AI development lab at IBM China. In these roles, Catherine demonstrated her ability to think horizontally and strategically to bring teams together to create innovative solutions for complex problems.

Catherine is an ambassador for the Women in Data Science organization (https: //www.widsconference.org/). She is passionate about inspiring and educating data scientists worldwide, particularly women in this field. She organized WiDS regional events over the past three years.

Catherine holds a master's degree in Computer Science from National University of Singapore, and a bachelor's degree in Computer Technology from Tsinghua University.

Eberhard Hechler是IBM德國研發實驗室的執行架構師。他是資料和人工智慧開發組織的成員，負責更廣泛的分析範圍，包括機器學習（ML）。在紐約的IBM金士頓實驗室工作了兩年多後，他從事軟體開發、性能優化、IT/解決方案架構和設計、Hadoop和Spark整合以及移動設備管理（MDM）等工作。

Eberhard在MVS平台上使用Db2進行測試和性能測量。他曾與IBM的各行業客戶合作，涉及數據和人工智慧、信息架構和行業解決方案等眾多主題。從2011年到2014年，他在IBM新加坡擔任通信部門的首席大數據架構師，負責亞太地區的工作。

Eberhard在德國和法國學習，擁有純數學碩士學位（Dipl.-Math.）和電機工程學士學位（Dipl.-Ing.（FH））。他是IBM技術學院的成員，並與他人合著了以下書籍：《企業MDM》、《企業信息架構的藝術》、《超越大數據》和《在企業中部署人工智慧》（Apress）。

Maryela Weihrauch是IBM Z技術銷售的資料和人工智慧開發組的IBM杰出工程師，也是客戶成功的領導者。她在系統、應用和數據庫設計方面對關聯數據庫有豐富的經驗。她與世界各地的企業合作，幫助他們采用新的數據和分析技術。她在Db2 for z/OS開發中的前任職位涉及確定HTAP（混合事務和分析處理）的Db2 for z/OS策略，包括Db2 Analytics Accelerator策略和實施，以及Db2的應用啟用策略。

Maryela在全球各地的企業咨詢許多數據現代化項目，並帶領一項工作，開發一種方法來根據數據架構決策標準確定給定應用程序的最佳數據架構。

Maryela在德國Chemnitz的技術大學和美國加利福尼亞州立大學奇科分校獲得了兩個計算機科學碩士學位。她擁有多項專利，是IBM技術學院的成員。她經常在世界各地的會議上分享自己的經驗。

Yan（Catherine）Wu是IBM矽谷實驗室的計劃總監。她是一位工程領導者，對數據治理、人工智慧（AI）、機器學習（ML）、企業設計思維和實用產品營銷有深入的專業知識。她在與大型客戶合作中積累了豐富的經驗，發現數據治理和AI的應用案例，探索最新技術如何應用於解決現實商業挑戰，並將這些技術應用於加速企業數字化轉型。她在與全球分佈的開發、設計和產品管理團隊合作時，將客戶需求轉化為軟體解決方案方面具有卓越的成績。

在目前在IBM美國的職位之前，Catherine是IBM中國的資料和人工智慧開發實驗室的實驗室主任。在這些職位上，Catherine展示了她橫向和戰略思維的能力，將團隊聚集在一起，為複雜問題創造創新解決方案。

Catherine是Women in Data Science組織的大使。她熱衷於激勵和教育全球的數據科學家，特別是這個領域的女性。她在過去三年組織了WiDS的區域活動。

Catherine擁有新加坡國立大學的計算機科學碩士學位和加利福尼亞州立大學的計算機技術學士學位。