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Managing Data as a Product: Design and build data-product-centered socio-technical architectures
Learn everything you need to know to manage data as a product and shift toward a more modular and decentralized socio-technical data architecture, capable of delivering business value in an incremental, measurable, and sustainable way
Key FeaturesLeverage data-as-product to unlock the modular platform potential and fix flaws in traditional monolithic architecturesIdentify, implement, and operate data products throughout their life cycleDesign and execute a successful strategy centered around data products in your organizationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTraditional 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. 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 also guides you through the design and implementation of 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 importance in fully leveraging modern generative AI solutions.
Upon completing the book, you’ll have gained a comprehensive understanding of product-centric data architecture and the necessary steps to begin adopting this modern approach to data management.
What you will learnRecognize challenges in scaling monolithic data platforms, including cognitive load, tech debt, and maintenance costsDiscover the benefits of adopting a data-as-a-product approach for scalability and sustainabilityGain insights into managing the data product lifecycle, from inception to decommissioningAutomate data product lifecycle management using a self-serve platformImplement an incremental, value-driven strategy for transitioning to data-product-centric architecturesMaster data modeling in distributed environments to enhance GenAI-based use casesWho this book is forIf you’re an experienced data engineer, data leader, architect, or practitioner thinking about your data architecture and how to design one that enables your organization to get the most value from your data in a sustainable and scalable way, this book is for you. Staff engineers, product managers, and other software engineering leaders and executives will also find this book useful. Familiarity with basic data engineering principles and practices is assumed.
Table of ContentsFrom Data as a by-product to Data as a ProductData ProductsData Product-Centered ArchitecturesIdentifying Data Products and Prioritizing DevelopmentsDesigning and Implementing Data ProductsOperating Data Products in ProductionAutomating Data Product's Lifecycle ManagementMoving through the adoption journeyTeam Topologies and Data Ownership at ScaleDistributed Data ModelingData Product Strategy and
Key FeaturesLeverage data-as-product to unlock the modular platform potential and fix flaws in traditional monolithic architecturesIdentify, implement, and operate data products throughout their life cycleDesign and execute a successful strategy centered around data products in your organizationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTraditional 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. 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 also guides you through the design and implementation of 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 importance in fully leveraging modern generative AI solutions.
Upon completing the book, you’ll have gained a comprehensive understanding of product-centric data architecture and the necessary steps to begin adopting this modern approach to data management.
What you will learnRecognize challenges in scaling monolithic data platforms, including cognitive load, tech debt, and maintenance costsDiscover the benefits of adopting a data-as-a-product approach for scalability and sustainabilityGain insights into managing the data product lifecycle, from inception to decommissioningAutomate data product lifecycle management using a self-serve platformImplement an incremental, value-driven strategy for transitioning to data-product-centric architecturesMaster data modeling in distributed environments to enhance GenAI-based use casesWho this book is forIf you’re an experienced data engineer, data leader, architect, or practitioner thinking about your data architecture and how to design one that enables your organization to get the most value from your data in a sustainable and scalable way, this book is for you. Staff engineers, product managers, and other software engineering leaders and executives will also find this book useful. Familiarity with basic data engineering principles and practices is assumed.
Table of ContentsFrom Data as a by-product to Data as a ProductData ProductsData Product-Centered ArchitecturesIdentifying Data Products and Prioritizing DevelopmentsDesigning and Implementing Data ProductsOperating Data Products in ProductionAutomating Data Product's Lifecycle ManagementMoving through the adoption journeyTeam Topologies and Data Ownership at ScaleDistributed Data ModelingData Product Strategy and
652 pages, Kindle Edition
Published November 29, 2024
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