Fabric Is Medallion‑First, Not Medallion‑Only

If you work with Microsoft Fabric long enough, it’s easy to come away with the impression that “real” Fabric means “medallion everywhere.” The official docs walk through Bronze, Silver, and Gold patterns for lakehouses. The learning paths lean on medallion as the canonical example. Fabric clearly makes medallion a first‑class citizen. 

But that doesn’t mean your data platform – or your data products – must be medallion‑shaped.

In a world of managed, domain‑aligned data products and Data Mesh thinking, what matters most is the contract at the edges: the inputs you accept, the outputs you guarantee, and the behaviors you commit to over time. Inside the boundary of a data product, you have more architectural freedom than many teams allow themselves.

In this post, I’ll walk through three ideas:

Fabric is medallion‑forward, but not medallion‑only. For data products, inputs and outputs matter far more than internal state. Internal architecture should serve engineering excellence, not a single prescriptive pattern – illustrated with small examples from financial services, wealth management, and insurance.

By the end, the goal is simple: when you design a Fabric data product, you should feel comfortable treating medallion as one option in a toolbox, not as a mandatory religion.

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Spec‑Driven Development: Make the Specification the First Commit

If your acceptance criteria live in a comment thread, they’re not requirements—they’re opinions. Spec‑driven development (SDD) turns those opinions into executable truth so code, tests, docs, and operations move in lockstep.

Building on our split between functional and nonfunctional requirements, this follow‑up introduces spec‑driven development: what it is, why it reduces drift, and how to run it inside agile without ceremony. We’ll connect behavior specs, API contracts, data schemas, and quality budgets to lightweight gates in CI/CD and SLOs in production. By the end, you’ll have a “small slice” pattern you can ship next sprint. This is where spec-driven development meets Agile, DevOps, and APIs.

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From Substitution to Outcomes: How AI and SAMR Are Forcing a Rethink of Development Strategy

We like to say we’ve “transformed” how work gets done. But if you look closely at many enterprise systems, you still see the outline of a paper form hiding under a slick UI.

We replaced paper with terminals, terminals with web apps, web apps with SaaS—and then pointed automation at the whole stack. In too many places, we’ve simply substituted one medium for another, without asking whether the underlying process still makes sense.

In this post, I’ll do three things:

  • Introduce the SAMR framework as a way to think about how technology shapes work.
  • Show how many business processes sit on layers of substitution going all the way back to paper.
  • Explain how AI enables goal‑based processes, where we define the outcome and how we’ll know we’ve met it, and let AI figure out the path—without prescriptive step‑by‑step code.

And along the way, we’ll confront an uncomfortable idea: some of your “modern” processes may still be driven by the personal preferences of someone who retired more than fifty years ago.

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Functional vs. Nonfunctional Requirements: Making the Split Work in Agile

If you’ve ever shipped a feature that “works” and still disappointed users, you’ve met the gap between what a system does and how well it does it. That gap is the space nonfunctional requirements occupy—and it’s where agile teams win or lose product trust.

In this continuation of our requirements series, we’ll clarify the difference between functional and nonfunctional requirements, show how to make nonfunctional requirements measurable, and connect both to practical agile habits—user stories, acceptance criteria, Definition of Done, SLOs, and pipeline checks. By the end, you’ll have a lightweight pattern you can apply this sprint. This is where #RequirementsEngineering meets Agile and DevOps.

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How Entra’s Agent Registry and Purview Team Up to Conquer Agent Sprawl

AI agents are showing up everywhere in the enterprise: Copilot add‑ins, line‑of‑business copilots built in Studio, “helper” bots glued onto SaaS apps, home‑grown automations running in the background. Individually, each one looks harmless. Collectively, they turn into something more dangerous: agent sprawl.

You get dozens (soon hundreds) of agents with overlapping responsibilities, inconsistent permissions, and no clear answer to a basic question: Which agents are touching my critical data, and under what guardrails?

Microsoft’s answer is starting to crystallize:

  • Microsoft Entra Agent Registry as the single source of truth for agent identity and metadata.
  • Microsoft Purview for Agents as the enforcement layer for data protection, DLP, insider risk, and compliance—using those identities as first‑class policy subjects.
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Beyond the Ontology: How the Rest of Fabric IQ Turns Meaning into Action

Yesterday we went deep on Fabric IQ’s Ontology—the shared vocabulary that teaches Microsoft Fabric how your business actually talks. Today we’ll zoom out to everything else: the graph that lets insights travel across relationships, the agents that answer questions and watch your operations in real time, and the governance and integration that make it usable at scale.

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From Tables to Meaning: A Deep Dive into Microsoft Fabric IQ’s Ontology (Preview)

AI agents don’t fail for lack of data—they fail for lack of meaning. Microsoft Fabric IQ’s new ontology capability tackles that head‑on by modeling the business concepts, relationships, and rules that live across your estate, then binding them to live data so agents (and people) can ask better questions and take smarter action.

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Start With Meaning: Elevating the Ontological Layer Above Your Semantic Layer

Your metrics are only as reliable as your nouns. If “customer,” “order,” or “revenue” shift between teams or tools, analytics becomes negotiation instead of decision. The way out is to put meaning first—an ontological layer that anchors everything—and then let the semantic layer deliver that meaning at speed and scale.

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SQL Server 2025 at Ignite: Why This Release Matters—and What to Do Next

In brief: SQL Server 2025 is generally available with built‑in AI, major developer conveniences, sturdier performance/availability behaviors, and licensing/edition changes that lower the cost of entry. Below I frame the release around three themes—AI + developer experienceperformance + resilience, and product/edition shifts—and close with concrete first steps you can act on today.

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SAP Business Data Cloud Connect for Microsoft Fabric: The New Backbone of Your Data‑Product Strategy

SAP and Microsoft have just taken away one of the biggest excuses for slow analytics and AI on SAP: “We can’t move that data safely or reliably enough.”

At Microsoft Ignite 2025, they announced SAP Business Data Cloud (BDC) Connect for Microsoft Fabric—a new capability that lets you share SAP Business Data Cloud data products and Microsoft Fabric data sets bi‑directionally, with zero‑copy, and have those products show up natively in OneLake and back in BDC.

Planned for general availability in Q3 2026, this isn’t “yet another connector.” It’s the missing link between SAP’s data‑product‑centric Business Data Cloud and Microsoft’s Fabric platform. It’s also where SAP Databricks, Azure Databricks, and Fabric line up as peers rather than competitors.

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