Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh.

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. Get a complete introduction to data mesh principles and its constituents Design a data mesh architecture Guide a data mesh strategy and execution Navigate organizational design to a decentralized data ownership model Move beyond traditional data warehouses and lakes to a distributed data mesh

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance.

"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.

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

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.

Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization