Data Fabric and Data Mesh Approaches with AI: A Guide to Ai-Based Data Cataloging, Governance, Integration, Orchestration, and Consumption (Paperback)
暫譯: 數據織物與數據網格方法:基於AI的數據目錄、治理、整合、編排與消費指南(平裝本)

Hechler, Eberhard, Weihrauch, Maryela, Wu

  • 出版商: Apress
  • 出版日期: 2023-04-01
  • 售價: $2,100
  • 貴賓價: 9.5$1,995
  • 語言: 英文
  • 頁數: 427
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484292529
  • ISBN-13: 9781484292525
  • 相關分類: 人工智慧大數據 Big-data雲端運算
  • 立即出貨 (庫存=1)

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商品描述

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.

 

商品描述(中文翻譯)

了解現代數據結構(data fabric)和數據網格(data mesh)概念,利用基於人工智慧(AI)的自助數據發現和交付能力、多種智能數據整合風格,以及自動化的統一數據治理——這些都是為了在混合雲環境中提供「數據作為產品」(data as a product)。

本書教您如何成功部署最先進的數據網格解決方案,並全面了解數據結構架構如何利用人工智慧(AI)和機器學習(ML)進行自動化的元數據管理和自助數據發現與消費。您將學習數據結構和數據網格如何與其他概念(如 DataOps、MLOps、AIDevOps 等)相關聯。本書包含許多範例,展示如何現代化數據消費,以實現類似購物的數據體驗(數據作為產品)。

在本書結束時,您將理解數據結構的概念和架構,並與自動化統一數據治理和合規性、企業信息架構、人工智慧和混合雲環境、智能目錄和元數據管理等主題相關聯。

您將學到的內容:

- 發現成功實施數據結構架構和數據網格解決方案的最佳實踐和方法
- 理解關鍵的數據結構能力,例如自助數據發現、智能數據整合技術、智能目錄和元數據管理,以及可信的人工智慧
- 認識數據結構在加速數位轉型和民主化數據訪問中的重要性
- 深入探討重要的數據結構主題,解決當前的數據結構挑戰
- 在企業背景下全面理解數據結構和數據網格概念
- 熟悉數據結構和數據網格的商業利益

本書適合對在企業內部部署現代數據結構架構和數據網格解決方案感興趣的任何人,包括 IT 和商業領導者、數據治理和數據辦公室專業人員、數據管理者和工程師、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理解。

作者簡介

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.

 

作者簡介(中文翻譯)

**Eberhard Hechler** 是 IBM 德國研發實驗室的執行架構師。他是數據與人工智慧開發組織的成員,負責更廣泛的分析範疇,包括機器學習 (ML)。在 IBM 纽约的 Kingston Lab 工作超過兩年後,他從事軟體開發、性能優化、IT/解決方案架構與設計、Hadoop 和 Spark 整合,以及行動裝置管理 (MDM)。

Eberhard 曾在 MVS 平台上使用 Db2,專注於測試和性能測量。他與來自各行各業的 IBM 客戶在全球範圍內合作,涉及數據與人工智慧、資訊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 IBM 軟體集團亞太地區通訊部門的首席大數據架構師。

Eberhard 在德國和法國學習,擁有純數學的碩士學位 (Dipl.-Math.) 和電機工程的學士學位 (Dipl.-Ing. (FH))。他是 IBM 技術學院的成員,並共同撰寫了以下書籍:《Enterprise MDM》、《The Art of Enterprise Information Architecture》、《Beyond Big Data》和《Deploying AI in the Enterprise》(Apress)。

**Maryela Weihrauch** 是 IBM Z 技術銷售的數據與人工智慧開發組的 IBM 傑出工程師,並擔任客戶成功領導者。她在關聯式數據庫的系統、應用程式和數據庫設計方面擁有豐富的經驗。她與全球企業合作,幫助他們採用新的數據和分析技術。她在 Db2 for z/OS 開發中的前期角色涉及為 HTAP(混合交易和分析處理)確定 Db2 for z/OS 策略,包括 Db2 分析加速器策略和實施,以及 Db2 的應用啟用策略。

Maryela 為全球企業提供有關許多數據現代化計劃的諮詢,並領導一項努力,開發一種方法論,以根據數據架構決策標準確定給定應用程式的最佳數據架構。

Maryela 擁有德國化學工業大學和美國加州州立大學奇科分校的計算機科學碩士學位。她擁有多項專利,並且是 IBM 技術學院的成員。她經常在全球各地的會議上分享她的經驗。

**Yan (Catherine) Wu** 是 IBM 硅谷實驗室的計劃總監。她是一位工程領導者,擁有數據治理、人工智慧 (AI)、機器學習 (ML)、企業設計思維和務實產品行銷的深厚專業知識。她在與大型客戶合作方面擁有豐富的經驗,發掘數據治理和 AI 的使用案例,探索最新技術如何應用於解決現實商業挑戰,並部署這些技術以加速企業數位轉型。她在將客戶需求轉化為軟體解決方案方面有著良好的記錄,並與全球分散的開發、設計和產品管理團隊協作。

在目前的 IBM 美國職位之前,Catherine 是 IBM 中國數據與人工智慧開發實驗室的實驗室主任。在這些角色中,Catherine 展示了她橫向和戰略性思考的能力,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 組織的代言人(https://www.widsconference.org/)。她熱衷於啟發和教育全球的數據科學家,特別是這個領域的女性。她在過去三年中組織了 WiDS 區域活動。

Catherine 擁有新加坡國立大學的計算機科學碩士學位,以及清華大學的計算機技術學士學位。