Conference Report – EduDataSci – Educating the world about data and leadership

A Data Product Is an Engine, Not a Table: FabCon 2026, Databricks, Fabric, and the Case for Interoperability

FabCon and SQLCon 2026 made the Microsoft Fabric and Azure Databricks story more concrete. The headline changes were not cosmetic. Microsoft moved zero-copy access to OneLake data from Azure Databricks into public preview, made Direct Lake in OneLake generally available, kept expanding Databricks-to-Fabric mirroring, and then pushed shortcut transformations into general availability in April. Put plainly, the platform story is moving away from “pick one stack forever” and toward “publish governed products that multiple engines can use.”

That matters because too many teams still call a dataset a product. Microsoft’s current guidance is more precise than that. A data product has defined shape, interfaces, maintenance expectations, and refresh cycles. It is processed for analytical use, and it should be discoverable, secure, interoperable, and valuable enough to serve downstream consumers without forcing them back into raw source complexity. The OneLake Catalog is now described in the Cloud Adoption Framework as a unified access surface for approved data products across Fabric and external processing platforms such as Databricks.

That is where Quantum Regression is a useful teaching device. I am not using it here as a product feature – or even going into the technology -, I am using it as a way to think about how data products enable modular expansions to the data estate. In this framing, a data product is an independent transformation: it accepts ingestions, applies logic, enforces quality and policy, and surfaces results. The file, table, semantic model, or API is only the current observable state of that transformation. The product is the operator, not the artifact. That is the mindset shift that helps make interoperability useful instead of merely fashionable.

After FabCon: What Agentic Apps on Microsoft Fabric Could Actually Look Like in Insurance and Wealth Management

The real test for agentic AI is not whether it can answer a question. It is whether it can answer the right question, with the right data, under the right controls, and then move work forward without creating new risk. That is why Microsoft’s March 12 piece on operationalizing agentic applications mattered. It shifted the conversation away from chatbot theater and toward architecture, telemetry, governance, and action. Since FabCon Atlanta in March 2026, that story has become more concrete: Microsoft used the event to frame trusted AI around OneLake, Real-Time Intelligence, Fabric IQ, and AI agents; said Fabric data agents are now generally available; introduced Fabric Remote MCP in preview; and announced Planning in Fabric IQ.

Just as important, the weeks immediately after FabCon filled in several missing pieces. Microsoft says Fabric IQ now supports Azure Private Link integration, and it also says Fabric IQ ontology will be exposed through public MCP endpoints. Microsoft added Ontology Rules with Fabric Activator, documented Business Events in Real-Time Intelligence, published source control and deployment-pipeline guidance for Fabric data agents, and, in April 2026, announced shortcut transformations as generally available. Taken together, those updates make Fabric’s agent story look less like a promising concept and more like an emerging operating model.

That matters because Fabric IQ is no longer just a semantic side note. Microsoft now describes IQ as a workload spanning ontology, plan, graph, data agents, operations agents, and semantic models. Ontology defines entity types, relationships, properties, and condition-action rules bound to real data. Graph adds relationship-centric analysis. Plan brings budgets, forecasts, and scenarios onto the same governed platform. And Fabric data agents can answer questions over lakehouses, warehouses, semantic models, KQL databases, ontologies, and Microsoft Graph in Fabric.

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From Chunks to Queries—Ignite 2025 Update: Fabric Data Agents, RAG, and the New IQ Layer

Monday, 9:02 a.m. The CFO pings: “What was Q3 gross margin by region—and did audit call out any risks?” Your RAG bot shines on PDFs and wiki pages, but it can’t compute a number you’d put on a KPI card. After Ignite 2025, the answer is cleaner than ever: let a Fabric Data Agent generate and run a governed query for the metric, and let your RAG retriever bring back the one‑sentence risk note. One conversation; two specialized tools; auditable answers.

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 experience, performance + resilience, and product/edition shifts—and close with concrete first steps you can act on today.

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.

Azure HorizonDB at Ignite 2025: What It Is, Why It Matters, and How to Think About It

Microsoft used Ignite 2025 to put a new flag in the ground for Postgres at cloud scale. Azure HorizonDB—branded as “HorizonDB”—promises the elasticity of a cloud-native architecture, the familiarity of PostgreSQL, and integrated AI features that shorten the path from schema to shipped app. Here’s what was announced, why it matters, and how to evaluate it for your stack.

Azure DocumentDB Is Back—And Open: Why the Ignite 2025 Launch Matters

If you’ve been around Azure long enough, the name “DocumentDB” triggers déjà vu. But at Microsoft Ignite (Nov. 18–21, 2025), DocumentDB returned with a different meaning: an open‑source, Linux Foundation–governed, MongoDB‑compatible engine now powering a first‑party Azure service. Here’s why that matters—and where it fits in your data strategy.

Ignite 2025 Beyond the Data Platform: How Microsoft Is Turning Everything Into an Agent Platform

Microsoft Ignite 2025 is officially the “Frontier firm” show—agents everywhere, all at once. If you’ve been tracking the data platform news (Fabric, databases, OneLake), you already know that story. This post looks at the rest of the landscape: Windows, Microsoft 365, Teams, Edge, security, Azure infra, and the growing partner ecosystem that’s rapidly filling in the agent-shaped gaps.

I’ll walk through the major non‑data platform announcements and highlight where Microsoft and partners are quietly reshaping the application, OS, and security layers around #Copilot, #AgenticAI, and #MSIgnite. Then we’ll close with what this means for architects and teams trying to build something coherent on top of all of this.

Microsoft Ignite – Wrap Up and Data Platform News

It’s always hard to sit down and fully absorb the information you get at a conference like Microsoft Ignite. You spend a week living in and around the tech world, just trying to drink in everything and interact with as many people as possible. I’m still doing more than a little digesting about what all of these announcements last week have meant, but I’m excited about quite a few of them, especially on the data platform side.

FabCon Feature: Fabric Real‑Time Intelligence

Real‑Time Intelligence (RTI) is the part of Fabric that treats events and logs as first‑class citizens: you connect live streams, shape them, persist them, query them with KQL or SQL, visualize them, and trigger actions—all without leaving the SaaS surface. Concretely, RTI centers on Eventstream (ingest/transform/route), Eventhouse (KQL databases), Real‑Time Dashboards / Map, and Activator (detect patterns and act). That tight loop—capture → analyze → visualize/act—now covers everything from IoT telemetry to operational logs and clickstream analytics.