We'll delve into the rationale behind and the implications of treating data as a product. We'll meticulously examine the various components constituting a data product, beginning with the interfaces accessible to external consumers, navigating through internal data management applications, and culminating in the infrastructure essential for operating it in production. Ultimately, we'll showcase the characteristics and capabilities that a data architecture centered on data products must have to strike a balance between the agility required for scalability and the governance necessary for sustainability.
- Chapter 01 - From data as a by-product to data as a product
- Chapter 02 - Data product’s anatomy
- Chapter 03 - Data product-centered architectures
We will explore how to manage a data product throughout its lifecycle. We'll begin by examining how to identify the data products to develop, prioritize them using a business-case-driven approach, and model them based on requirements. We will then look at how to handle releases and data product management in a production environment by adapting common DevOps practices to the data context. Finally, we will explore how to automate lifecycle management through a self-serve platform analyzing its core capabilities, architecture, and implementation options (make vs. buy).
- Chapter 04 - Identifying data products and prioritizing developments
- Chapter 05 - Designing and implementing data products
- Chapter 06 - Operating data products in production
- Chapter 07 - Automating data product’s lifecycle management
We will explore how to design and implement an incremental, value-driven strategy for successfully adopting architectures centered on data products. We'll analyze key elements of the strategy and how to define them in the early stages of the adoption journey. Understanding how to kickstart the initiative and secure buy-in from key stakeholders will be covered. Subsequently, we'll examine how to scale adoption across the enterprise by fostering a data-driven culture based on the principle of managing data as a product. Finally, we'll delve into data modeling in a distributed environment, emphasizing how modeling, both at the physical and conceptual levels, is a crucial element for fully leveraging the potential offered by modern generative AI solutions.