Data Products And The Data Mesh
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Data Products and the Data Mesh
"Data Products and the Data Mesh" is a comprehensive guide that explores the emerging paradigm of the data mesh and its implications for organizations navigating the data-driven landscape. This book equips readers with the knowledge and insights needed to design, build, and manage effective data products within the data mesh framework. The book starts by introducing the core concepts and principles of the data mesh, highlighting the shift from centralized data architectures to decentralized, domain-oriented approaches. It delves into the key components of the data mesh, including federated data governance, data marketplaces, data virtualization, and adaptive data products. Each chapter provides in-depth analysis, practical strategies, and real-world examples to illustrate the application of these concepts. Readers will gain a deep understanding of how the data mesh fosters a culture of data ownership, collaboration, and innovation. They will explore the role of modern data architectures, such as data marketplaces, in facilitating decentralized data sharing, access, and monetization. The book also delves into the significance of emerging technologies like blockchain, AI, and machine learning in enhancing data integrity, security, and value creation. Throughout the book, readers will discover practical insights and best practices to overcome challenges related to data governance, scalability, privacy, and compliance. They will learn how to optimize data workflows, leverage domain-driven design principles, and harness the power of data virtualization to drive meaningful insights and create impactful data products. "Data Products and the Data Mesh" is an essential resource for data professionals, architects, and leaders seeking to navigate the complex world of data products within the data mesh paradigm. It provides a comprehensive roadmap for building a scalable, decentralized, and innovative data ecosystem that empowers organizations to unlock the full potential of their data assets and drive data-driven success.
Designing Great Data Products
Author: Jeremy Howard
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2012-03-23
In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines like Amazon's. Prediction technology can be interesting and mathematically elegant, but we need to take the next step: going from recommendations to products that can produce optimal strategies for meeting concrete business objectives. We already know how to build these products: they've been in use for the past decade or so, but they're not as common as they should be. This report shows how to take the next step: to go from simple predictions and recommendations to a new generation of data products with the potential to revolutionize entire industries.
Software Architecture: The Hard Parts
There are no easy decisions in software architecture. Instead, there are many hard parts--difficult problems or issues with no best practices--that force you to choose among various compromises. With this book, you'll learn how to think critically about the trade-offs involved with distributed architectures. Architecture veterans and practicing consultants Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani discuss strategies for choosing an appropriate architecture. By interweaving a story about a fictional group of technology professionals--the Sysops Squad--they examine everything from how to determine service granularity, manage workflows and orchestration, manage and decouple contracts, and manage distributed transactions to how to optimize operational characteristics, such as scalability, elasticity, and performance. By focusing on commonly asked questions, this book provides techniques to help you discover and weigh the trade-offs as you confront the issues you face as an architect. Analyze trade-offs and effectively document your decisions Make better decisions regarding service granularity Understand the complexities of breaking apart monolithic applications Manage and decouple contracts between services Handle data in a highly distributed architecture Learn patterns to manage workflow and transactions when breaking apart applications