Data Mesh: Building Scalable, Resilient, and Decentralized Data Infrastructure for the Enterprise. Part 2

By Tom Lesley

The book then goes on to provide a detailed guide to building a Data Mesh architecture, covering topics such as designing autonomous data domains, building a data product catalog, implementing federated governance, and managing data pipelines. The authors provide practical advice and real-world examples to help you understand the key concepts and apply them in your organization.

 

In addition to the technical aspects of building a Data Mesh architecture, the book also covers the organizational and cultural changes that are necessary to implement this approach successfully. The authors explain how to build a culture of data collaboration and democratization, how to establish clear roles and responsibilities for data management, and how to create a data-driven organization that is capable of making data-driven decisions.

 

Whether you are a data architect, a data engineer, a data scientist, or a business leader looking to improve your organization's data infrastructure, Data Mesh provides a comprehensive guide to building a scalable, resilient, and decentralized data architecture that can meet the demands of modern enterprise data management. With practical advice, real-world examples, and a detailed roadmap for implementation, this book is essential reading for anyone looking to take their organization's data infrastructure to the next level.

• Language: English
• Publisher: May Reads
• Release date: Apr 19, 2024
• ISBN: 9798224538959

Book preview

Table of Contents

Data Mesh: Building Scalable, Resilient, and Decentralized Data Infrastructure for the Enterprise. Part 2

Chapter 2: The Point of Inflection

Chapter 3: Before the Point of Inflection

Chapter 4: After the Point of Inflection

Chapter 6: The Logical Architecture

Chapter 7: The Multiplane Data Platform Architecture

Chapter 8: Data Mesh Execution Framework

Conclusion

Data Mesh

Building Scalable, Resilient, and Decentralized Data Infrastructure for the Enterprise

Part 2

Tom Lesley

Chapter 1: Implementing Data Mesh in the Enterprise

Case studies of successful Data Mesh implementation

Overcoming challenges in implementing Data Mesh

Best practices and guidelines for Data Mesh implementation

Lessons learned from Data Mesh implementation

Chapter 2: The Point of Inflection

Three Main Principles of Data Mesh

A Difficult Journey to the Inflection Point

The Great Divide of Data

Approaching the Plateau of Return

Chapter 3: Before the Point of Inflection

Chapter4: After the Point of Inflection

Respond Gracefully to Change in a Complex Business

Increase the Ratio of Value from Data to Investment

The Role of People

Bottom-Up Architecture

Product Management

Governance and Standards

Sustain Agility in the Face of Growth

Chapter 5: Managing and Operating a Data Mesh

Managing Data Products and Services

Monitoring and Observability of Data Mesh

Updating and Maintaining Data Mesh

Managing and Mitigating Risks in Data Mesh

Chapter 6: The Logical Architecture

The Operational Architecture

The Logical Architecture

The Solution Design

The Data Mesh as a Cultural Concept

Data Mesh as a Framework for Building a Data Architecture

Domain-Oriented Analytical Data Sharing Interfaces

Data Product as an Architecture Quantum

The Multiplane Data Platform

Embedded Computational Policies

Control Port

Chapter 7: The Multiplane Data Platform Architecture

Chapter 8: Data Mesh Execution Framework

The Framework

Chapter 9: The Future of Data Mesh

Emerging trends and technologies in Data Mesh

The role of Data Mesh in shaping the future of data architecture

The impact of Data Mesh on the enterprise landscape

Conclusion

Chapter 1: Implementing Data Mesh in the Enterprise

Case studies of successful Data Mesh implementation

Zalando: Zalando, the European online fashion retailer, implemented a Data Mesh to improve their data management capabilities. They created cross-functional teams around data products, with each team being responsible for the entire lifecycle of a specific data product, including data modeling, transformation, storage, and governance. By adopting this approach, they were able to reduce data silos and increase data quality, resulting in faster and more accurate decision-making.

ThoughtWorks: ThoughtWorks, a global software consultancy, implemented a Data Mesh to improve their data-driven decision-making capabilities. They created a decentralized data platform with autonomous cross-functional teams responsible for building and maintaining data products. This approach allowed them to scale their data infrastructure while maintaining flexibility and agility.

Fidelity Investments: Fidelity Investments, a financial services company, implemented a Data Mesh to improve their data analytics capabilities. They created a centralized data catalog and a decentralized data governance model that enabled autonomous teams to own and manage their own data products. This approach allowed them to better leverage data to gain insights and improve decision-making.

Siemens: Siemens, a multinational industrial conglomerate, implemented a Data Mesh to improve their data management capabilities. They created a data ecosystem with a federated architecture, where each business unit had its own data product and was responsible for its own data governance. This approach allowed them to reduce data silos and improve data quality while still maintaining centralized oversight.

ABN AMRO Bank: ABN AMRO Bank, a Dutch banking and financial services company, implemented a Data Mesh to improve their data capabilities. They created cross-functional teams around specific data domains, with each team responsible for the entire lifecycle of a data product, including data ingestion, storage, processing, and governance. This approach enabled them to reduce data silos and improve data quality, resulting in better decision-making and customer experiences.

Equinor: Equinor, a Norwegian energy company, implemented a Data Mesh to improve their data management and analytics capabilities. They created a data lake with a federated architecture, where each business unit had its own data product and was responsible for its own data governance. This approach allowed