Название: Data Mesh: Principles, Patterns, Architecture, and Strategies for Data-Driven Decision Making
Автор: Pradeep Menon
Издательство: BPB Publications
Год: 2024
Страниц: 282
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
Data Mesh: The future of data architecture! "Data Mesh: Principles, patterns, architecture, and strategies for data-driven decision making" introduces Data Mesh which is a macro data architecture pattern designed to harmonize governance with flexibility. This book guides readers through the nuances of Data Mesh topologies, explaining how they can be tailored to meet specific organizational needs while balancing central control with domain-specific autonomy. The book delves into the Data Mesh governance framework, which provides a structured approach to manage and control decentralized data assets effectively. It emphasizes the importance of a well-implemented governance structure that ensures data quality, compliance, and access control across various domains. Additionally, the book outlines robust data cataloging and sharing strategies, enabling organizations to improve data discoverability, usage, and interoperability between cross-functional teams. Securing Data Mesh architectures is another critical focus.
Автор: Pradeep Menon
Издательство: BPB Publications
Год: 2024
Страниц: 282
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
Data Mesh: The future of data architecture! "Data Mesh: Principles, patterns, architecture, and strategies for data-driven decision making" introduces Data Mesh which is a macro data architecture pattern designed to harmonize governance with flexibility. This book guides readers through the nuances of Data Mesh topologies, explaining how they can be tailored to meet specific organizational needs while balancing central control with domain-specific autonomy. The book delves into the Data Mesh governance framework, which provides a structured approach to manage and control decentralized data assets effectively. It emphasizes the importance of a well-implemented governance structure that ensures data quality, compliance, and access control across various domains. Additionally, the book outlines robust data cataloging and sharing strategies, enabling organizations to improve data discoverability, usage, and interoperability between cross-functional teams. Securing Data Mesh architectures is another critical focus.