Data Products – EduDataSci – Educating the world about data and leadership

Foundational + Derived Data Products in a Data Mesh

A data mesh is a sociotechnical approach to analytical data that decentralizes responsibility to business domains while standardizing the way data is produced and consumed. It’s grounded in four principles: domain ownership, data as a product, a self‑serve data platform, and federated governance. In practice, it asks each domain team to publish data as a product—discoverable, trustworthy, and operable—while a common platform automates cross‑cutting rules (access, lineage, quality, security).

Zhamak Dehghani frames a data product as an architectural quantum: the smallest independently deployable unit that bundles data, code, metadata, and policy, with a versioned contract and a clear interface (APIs or governed views). Treating both foundational and derived products as quanta is the key to decoupled evolution without breaking interoperability.

Data Products in Fabric, Part 3: Why Fabric Is Ideal, What to Expose, and How to Govern (with zero‑copy patterns)

In Parts 1–2, we framed a data product as a reusable, self‑contained package that bundles data, metadata, access methods, and governance to deliver an outcome—discoverable, interoperable, and managed like software. We also separated foundational (stable, domain‑anchored) from derived (composed/enriched for specific use‑cases) and showed how composition is the workhorse of value delivery.

This third part makes that guidance concrete on Microsoft Fabric: why Fabric is a natural home for data products, which product types you can expose, and how to govern and compose them—including zero‑copy patterns and two near‑term preview capabilities: Materialized Lake Views and Shortcut Transformations.

Foundational and Derived Data Products: Practical Guidance for Architects and Data Leaders

As we discussed previously, a data product is a reusable, self‑contained package that bundles data, metadata, access methods, and governance to deliver a clear outcome to users or other systems. Treating data as a product implies product management disciplines (contracts, SLOs, versioning, observability) and an emphasis on discoverability, interoperability, and security.

Within modern mesh-aligned architectures, data products must be interoperable and composable so they join predictably and can be assembled into higher‑order solutions. This is a first‑principles characteristic, not a nice‑to‑have.

Data Products Demystified: What They Are and Why They Matter

Data products are becoming a hot topic across industries, from classrooms to oil fields to trading floors. Yet the term “data product” can be confusing, conjuring images of complex databases or black-box AI. This blog post aims to clarify what a data product actually is in straightforward terms, and why it’s important for both technical and non-technical professionals. We’ll explore how data products turn raw data into useful tools, how they benefit organizations, and how they differ from other data concepts. Along the way, we’ll look at a couple examples to make the ideas concrete.