Managing Data as a Product: Design and build data-product-centered socio-technical architectures - Softcover

Andrea Gioia

Softcover

ISBN 10: 1835468535 ISBN 13: 9781835468531

Publisher: Packt Publishing, 2024

Synopsis

Learn everything you need to know to manage data as a product and shift toward a more modular and decentralized socio-technical data architecture to deliver business value in an incremental, measurable, and sustainable way

Key Features

• Leverage data-as-product to unlock the modular platform potential and fix flaws in traditional monolithic architectures
• Learn how to identify, implement, and operate data products throughout their life cycle
• Design and execute a forward-thinking strategy to turn your data products into organizational assets
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Traditional monolithic data platforms struggle with scalability and burden central data teams with excessive cognitive load, leading to challenges in managing technological debt. As maintenance costs escalate, these platforms lose their ability to provide sustained value over time. With two decades of hands-on experience implementing data solutions and his pioneering work in the Open Data Mesh Initiative, Andrea Gioia brings practical insights and proven strategies for transforming how organizations manage their data assets.

Managing Data as a Product introduces a modular and distributed approach to data platform development, centered on the concept of data products. In this book, you’ll explore the rationale behind this shift, understand the core features and structure of data products, and learn how to identify, develop, and operate them in a production environment. The book guides you through designing and implementing an incremental, value-driven strategy for adopting data product-centered architectures, including strategies for securing buy-in from stakeholders. Additionally, it explores data modeling in distributed environments, emphasizing its crucial role in fully leveraging modern generative AI solutions.

By the end of this book, you’ll have gained a comprehensive understanding of product-centric data architecture and the essential steps needed to adopt this modern approach to data management.

What you will learn

• Overcome the challenges in scaling monolithic data platforms, including cognitive load, tech debt, and maintenance costs
• Discover the benefits of adopting a data-as-a-product approach for scalability and sustainability
• Navigate the complete data product lifecycle, from inception to decommissioning
• Automate data product lifecycle management using a self-serve platform
• Implement an incremental, value-driven strategy for transitioning to data-product-centric architectures
• Optimize data modeling in distributed environments to enhance GenAI-based use cases

Who this book is for

If you’re an experienced data engineer, data leader, architect, or practitioner committed to reimagining your data architecture and designing one that enables your organization to get the most value from your data in a sustainable and scalable way, this book is for you. Whether you’re a staff engineer, product manager, or a software engineering leader or executive, you’ll find this book useful. Familiarity with basic data engineering principles and practices is assumed.

Table of Contents

1. From Data as a Byproduct to Data as a Product
2. Data Products
3. Data Product-Centered Architectures
4. Identifying Data Products and Prioritizing Developments
5. Designing and Implementing Data Products
6. Operating Data Products in Production
7. Automating Data Product Lifecycle Management
8. Moving through the Adoption Journey
9. Team Topologies and Data Ownership at Scale
10. Distributed Data Modeling
11. Building an AI-Ready Information Architecture
12. Bringing It All Together

About this title

About the Author

Andrea Gioia is a partner and CTO at Quantyca, a consulting firm specializing in data management, and co-founder of blindata.io, a SaaS platform for data governance and compliance. With over 20 years of experience, Andrea has led cross-functional teams delivering complex data projects across multiple industries. As CTO, he advises clients on defining and executing their data strategies. Andrea is a frequent speaker and writer, serving as the main organizer of the Data Engineering Italian Meetup and leading the Open Data Mesh Initiative. He is an active DAMA member and has been part of the DAMA Italy Chapter's scientific committee since 2023.

From the Back Cover

Traditional monolithic data platforms struggle to scale, overwhelming central teams with cognitive load and making technological debt hard to manage. As maintenance costs rise, these platforms lose their ability to deliver lasting value. Managing data as a product is a new paradigm, addressing platform complexity sustainably through a modular, socio-technical architecture designed to balance agility and composability.

In this book, you'll explore the rationale behind this paradigm shift, understand data products' core features and structure, and learn how to identify, develop, and operate them in a production environment. The book also guides you through designing and implementing an incremental, value-driven strategy for adopting data-product-centered architecture, including strategies for securing buy-in from stakeholders. Additionally, it explores data and knowledge modeling in distributed environments, emphasizing its importance in fully leveraging modern generative AI solutions.

Upon completing the book, you'll have a comprehensive understanding of data-product-centered architecture and the steps required to adopt this new data management paradigm.


What you will learn:

• Overcome the challenges in scaling monolithic data platforms, including cognitive load, tech debt, and maintenance costs
• Discover the benefit of adopting a data-as-a-product approach for scalability and sustainability
• Navigate the complete data product lifecycle, from inception to decommissioning
• Automate data product lifecycle management using a self-serve platform
• Implement an incremental, value-driven strategy for transitioning to data-product-centric architectures
• Optimize data modeling in distributed environments to enhance GenAI-based use cases

From the Inside Flap

Book's outline

Chapter 1, From data as a by-product to data as a product, shows how modularizing data architecture with data products solves recurring problems that make its sustainable evolution challenging over time

Chapter 2, Data product's anatomy, defines what a data product is, outlining its key characteristics and explaining the essential components that make it up, highlighting how each element contributes to its overall function and value.

Chapter 3, Data product-centered architectures, explores the foundational principles of a data product-centered architecture, analyzing the key operational and organizational capabilities required to manage it. We also compare other modern approaches like data mesh and data fabric with the data-as-product paradigm to highlight their similarities and key differences.

Chapter 4, Identifying data products and prioritizing developments, explains how to identify and prioritize data products using a value-driven approach. It starts by identifying relevant business cases through Domain-Driven Design and event storming, then shows how to define the data products needed to support those business cases.

Chapter 5, Designing and implementing data products, explores the process of designing a data product based on identified requirements, starting with techniques for defining scope, interfaces, and ecosystem relationships. It then examines the core components of a data product, their development process, and how to describe them with machine-readable documents. Finally, it analyzes the data flow, focusing on components responsible for sourcing, processing, and serving data.

Chapter 6, Operating data products in production, covers the entire lifecycle of a data product, from release to decommissioning. It introduces CI/CD methodologies, explores managing a data product in production with a focus on governance, observability, and access control, and discusses techniques for evolving and reusing data products in a distributed environment.

Chapter 7, Automating data product's lifecycle management, explains how to speed up the adoption of a data product-centric paradigm by creating a self-serve platform to mobilize the entire data ecosystem. It covers the platform's main features, how it improves the experience for developers, operators, and consumers, and the key factors in deciding whether to build, buy, or use a hybrid approach in implementing it.

Chapter 8, Moving through the adoption journey, covers the adoption of the data-as-a-product paradigm. It outlines the key phases of the process, exploring objectives, challenges, and activities for each stage. Finally, it discusses how to create a flexible data strategy that evolves with each phase, building on previous learnings.

Chapter 9, Team topologies and data ownership at scale, explains how to design an organizational structure for managing data as a product. It introduces the Team Topologies framework, including team types and interaction modes, and explores how to organize teams for efficient data product delivery. Finally, it looks at how to integrate these teams into the organization and decide between centralized or decentralized data management model.

Chapter 10, Distributed data modeling, examines data modeling in a decentralized, data product-centered architecture. It defines data models and emphasizes intentionality in modeling, then examines physical modeling techniques for distributed environments. Finally, it covers conceptual data modeling and its role in guiding the design and evolution of data products within a cohesive ecosystem.

Chapter 11, Building an AI-Ready Information Architecture, explores how to build an information architecture that maximizes the value of managed data, starting with developed data products. It covers how different planes of the information architecture add context to data and focuses especially on the knowledge plane, where shared conceptual models ensure semantic interoperability between data products. Finally, it explores how federated modeling teams can create and link conceptual models to physical data, forming an enterprise knowledge graph crucial for unlocking the potential of generative.

Chapter 12, Bringing It All Together, revisits key concepts from earlier chapters, tying them to the core beliefs about data management that inspired this book. It wraps up with practical advice for becoming a more successful data management practitioner.

Edition details

• Publisher: Packt Publishing
• Publication date: 2024
• ISBN 10: 1835468535
• ISBN 13: 9781835468531
• Binding: Paperback
• Language: English
• Number of pages: 368