Data Mesh

"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. The text explores comprehensive security strategies that protect data across different layers of the architecture, ensuring data integrity and protecting against breaches.

By implementing the strategies discussed, data professionals will strengthen their ability to safeguard sensitive information in a distributed environment, making this book a vital resource for anyone involved in data management, security, or governance.

KEY FEATURES

* Decentralize data with domain-oriented design.
* Enhance scalability and data autonomy.
* Implement robust governance across domains.

WHAT YOU WILL LEARN

* Understand the evolution and need for Data Mesh architectures.
* Learn the core principles and design for Data Mesh implementations.
* Identify and apply Data Mesh architectural patterns and components.
* Implement effective Data Mesh governance frameworks.
* Develop and execute a strategic data cataloging plan.
* Create comprehensive data-sharing strategies and security strategies within Data Mesh.

WHO THIS BOOK IS FOR

This book is ideal for data professionals, including chief data officers, chief analytics officers, chief information officers, enterprise data architects, data stewards, and data governance and compliance professionals.

1. Establishing the Data Mesh Context
2. Evolution of Data Architectures
3. Principles of Data Mesh Architecture
4. The Patterns of Data Mesh Architecture
5. Data Governance in a Data Mesh
6. Data Cataloging in a Data Mesh
7. Data Sharing in a Data Mesh
8. Data Security in a Data Mesh
9. Data Mesh in Practice
Appendix: Key terms

Pradeep Menon is an accomplished technology professional with over 20 years of extensive expertise in Data, AI, Analytics, and Cloud Computing. Currently serving as the CTO for Digital Natives in ASEAN at Microsoft, Pradeep is pivotal in spearheading the adoption and strategic implementation of Generative AI across the region. His career highlights a robust background with roles at Microsoft and Alibaba Cloud, where he successfully led major initiatives in data and AI, greatly enhancing business strategies and operational efficiency across Asia.

Pradeep’s approach seamlessly integrates high-level strategic discussions with C-suite executives and detailed technical implementations, making him a key figure in driving digital transformation. His technical and strategic acumen has resulted in significant revenue growth and enhanced competitive positioning for numerous enterprises.

A thought leader and visionary, Pradeep’s contributions extend beyond corporate borders. He is the acclaimed author of “Data Lakehouse in Action” and a revered voice on the international speaking circuit, illuminating pathways in technology with his insights. His academic credentials— an MS in Business Analytics from NYU Stern and an MBA from Strathclyde—marry technical prowess with strategic insight, underscoring his holistic approach to innovation and leadership in the digital age.