Why this is in my collection

From the publisher:

Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization. Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed.

Highlights

  • Scale forces federation, federation forces explicit contracts — Strengholt's core argument is that beyond a certain size you must distribute data ownership, and distribution only works when interfaces, schemas, and expectations are written down; implicit structure stops scaling long before data does.
  • Blueprints over improvisation — the book delivers reusable architectural patterns (data product architectures, integration styles, distribution patterns) rather than one reference design; pattern catalogs are what make architecture teachable and, eventually, generatable.
  • Metadata is the management plane — at scale, you govern through catalogs, lineage, and classification rather than through people who remember things; the same move MDDE makes when it puts the model, not the codebase, in charge.
  • Governance must be computational — policies for quality, security, and access have to execute as code at the boundaries, not live in PDF policy documents; rules as the third pillar, deployed rather than declared.
  • Domain boundaries are modeling decisions — how you cut the landscape into domains and data products determines everything downstream, which puts conceptual modeling back at the center of what looks like an infrastructure problem.
  • Master data and integration don't disappear in decentralization — Strengholt is honest that federating ownership doesn't dissolve the hard integration problems Data Vault-style layers exist to solve; someone still has to model the enterprise view.

Highlights on this page are generated with the help of AI.