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.
About the Author: 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.
