FabCon and SQLCon 2026

It is the third Fabcon event and first ever SQLCon in Atlanta this week. This week Microsoft didn't just make a small change, they made a larger shift. The convergence of where data lives and what data does has been the holy grail of database management and the biggest hurdle to AI isn't the model itself, it is the data ingest quality and the fragmentation of the estate. The announcements from the conference help answer the current chaos and complexity that exists. This is my take on the key shifts that will matter most when navigating the database landscape.

The Single Pane of Glass Arrives in the Database Hub

For years, we have managed our estates in silos, Azure SQL over here, Cosmos DB over there, and SQL Server on-premises (hopefully via Azure Arc) somewhere else. The new Database Hub in Microsoft Fabric (now in early access) is a game-changer for governance. It provides a unified view to explore and optimize the entire estate. But the real interest is the agent-assisted management. Using intelligent agents to reason over signals and explain why something changed. It keeps the human in the loop but removes the manual drudgery.

Microsoft IQ: The Semantic Layer for AI

One of the biggest announcements is how Fabric is becoming the intelligence layer for the enterprise.

•  Fabric IQ brings together live business data.
•  Work IQ pulls in productivity signals.
•  Foundry IQ captures institutional knowledge.

This is critical because AI agents are only as good as the context they have. By creating a unified semantic meaning, we are finally moving away from hunting for data and toward activating data.

OneLake is Closing the Gap on Silos

The OneLake vision continues to expand with more native mirroring capabilities (SharePoint lists and Dremio are now in preview; Oracle and SAP Datasphere are GA).

The standout for me, however, is the Shortcut transformations. The ability to shape data, like converting Excel to Delta tables, automatically as it connects to OneLake is a massive win for data quality. We know that without good quality data at the start, the AI journey hits a wall. These automated gatekeepers help ensure the lake doesn't become a swamp.

Mission Critical Apps with connected SQL and Fabric

With SQL Server 2025 growing faster than any previous version, the integration with Fabric is no longer a maybe. The announcements focused heavily on a converged platform that unifies transactional and analytical data.

For developers, the new Migration Assistant for SQL databases (using AI to resolve compatibility issues via DACPACs) is a pragmatic approach to modernization.

Beyond the Hype it is easy to get lost in the agentic AI buzzwords. But looking at the technical roadmap from FabCon/SQLCon, the focus is clearly on Usability, Empowerment, and Security.

We are moving toward a world where the database is taking a more active place in the business. Whether we are managing a legacy SQL estate or building a greenfield Fabric environment, the wall between our operational databases and analytics is getting less.

 You can read more about the announcements: 

FabCon and SQLCon 2026: Unifying databases and Fabric on a single data platform

Advancing Databases for the Next Generation of Applications

Inspirational STEM 1958-1968

From the moment I first understood the meaning of my mum’s , Joan Holt, school motto “Be strong and very courageous” I realised it wasn’t just a phrase she carried; it was a quiet force that shaped her life. Long before women in STEM were recognised or encouraged in the way they are today, she worked in a world of theoretical physics, numerical analysis, and early computing with a determination that still leaves me in awe. Hers was not the loud, celebrated courage of someone who set out to break barriers, but the steady, purposeful courage of someone who simply refused to accept that those barriers applied to her. With it being International Women’s Day last week we celebrated the women who paved the way and that reminds that one of those pioneers was my mum.

Her career reads like a living history of British computing. Her first days were working on IBM mainframes and analysing data, when computers filled whole rooms and printouts were the size of phone books to programming the Ferranti Mk1. She drafted manuals for the Elliott 503 and 4100, and solved problems with nothing but symbolic assembly code. She lived through the evolution of technology as few people did. She worked in rooms where magnetic tapes towered over her, where data meant punched cards and where a single mistake meant repunching a deck. She navigated machines that shook themselves off desks, deciphered the results of calculations that once took over 8 months, and wrote documentation that bridged engineers and the future operators. She was often the only woman in the room, one of only a handful among thousands of men at just nineteen. She simply worked hard proving herself indispensable through intelligence, persistence, and grace.

Today, on Mother’s Day, I think not only of the extraordinary work she did, but of the extraordinary woman she was. A role model who taught me that courage can be quiet, curiosity can be powerful, and that you can shape the world even if you never stand in the spotlight. While the world now celebrates women in STEM more visibly than ever, she lived those values when the path was far tougher and the recognition far thinner. Her achievements may sit in old manuals, early programs, and memories of rooms filled with tapes and valves, but her legacy is alive in me. I am proud beyond words to have been her daughter, and prouder still to share her personal story to help inspire future generations.

Fabric, Purview, and the New Shape of Enterprise Data Architecture

Fabric has reshaped the Microsoft data landscape by unifying analytics, engineering, and storage into a single experience. But unification alone does not create coherence. The real transformation happens when Fabric is paired with Purview. Together, they form an architecture where data movement, governance, and analytics operate as one system rather than disconnected components.

This convergence matters because modern data estates are too complex to govern manually. Data flows across pipelines, notebooks, semantic models, and AI workloads. Without integrated governance, organisations end up with pockets of visibility rather than a complete picture. Purview provides the lineage, classification, and policy enforcement that Fabric alone cannot deliver.

One of the most powerful aspects of this integration is the alignment between data products and governance. Fabric encourages teams to think in terms of products that are curated, reusable assets with clear ownership. Purview reinforces this by providing the metadata, stewardship, and controls that make data products trustworthy. Governance becomes part of the product lifecycle, not an afterthought.

This new architecture also supports hybrid and multi‑cloud realities. Many organisations are not all using Fabric, nor should they be. Purview’s ability to govern across environments ensures that Fabric becomes a strategic hub rather than a silo. The result is an architecture that is unified, not monolithic but flexible.

As organisations modernise their estates, the combination of Fabric and Purview will become the default pattern. It is not just a technical alignment but it is a governance first architecture for the AI era.

It is FABCON and SQLCON in Atlanta March 16 - 20, 2026. 

Purview and OneLake Govern tab change

The Purview Hub in Fabric insights have now moved to the OneLake catalog’s Govern tab. The change helps bring governance closer to where the data actually lives, rather than leaving them in a parallel experience that always that wasn't as helpful as it could have been. In the Govern tab, you now see the same posture summaries, recommended actions, and learning resources that were in Purview Hub, but framed within Fabric’s unified governance model. It is a cleaner, more coherent way of surfacing what core information about the health of your data estate.

Functionally, the Govern tab now gives you a consolidated view of governance status, recommended actions, sensitivity and endorsement insights, and links into deeper governance tooling. You can drill into items that need attention, track improvements over time, and understand how your organisation is using Fabric’s governance features. The experience also ties directly into the OneLake catalog, so governance isn’t an afterthought. It is embedded in the same place you explore, classify, and manage data assets.

Microsoft hasn’t yet published a formal retirement date for the Purview Hub. Fabric is now  presenting a single, coherent story about how organisations should understand and manage their data estate.

You can learn more about it here.

The Rise of Contextual Governance: Moving Beyond Static Rules

We have reached a point where models can learn faster than organisations can adapt. This creates a dangerous asymmetry: the technology evolves, but the governance, culture, and literacy lag behind. The result is a widening gap between capability and control. Organisations deploy increasingly powerful models without fully understanding their behaviour, limitations, or risks.

This gap is not caused by technology but by it’s caused by organisational inertia. Many teams still rely on outdated governance processes that cannot keep pace with continuous learning systems. Policies are static, reviews are infrequent, and oversight is reactive. Meanwhile, models evolve with every new dataset, every retraining cycle, and every shift in user behaviour.

The solution is not to slow the models but to accelerate organisational learning. Governance must become continuous, adaptive, and embedded into operational workflows. This means real‑time monitoring, dynamic policies, and stewardship that evolves alongside the data. It also means investing in literacy so that teams understand not just how to use AI, but how to question it.

When organisations learn as fast as their models, AI becomes a strategic advantage. When they don’t, AI becomes a liability. The choice on how to move forward is not technological, it is cultural.

The Governance Gap and Why Organisations Still Struggle to Operationalise Policy

Most organisations don’t have a policy problem, they have an operationalisation problem. Policies exist, but they’re not enforced, monitored, or embedded into workflows. Governance becomes a theoretical exercise rather than a practical one. Teams know what they should do, but the mechanisms to ensure they actually do it are missing.

This gap often emerges because governance is treated as documentation rather than behaviour. Policies are written in isolation, disconnected from the systems and processes they’re meant to govern. Without automation, policies rely on human discipline, and human discipline is inconsistent at best.

Microsoft Purview helps close this gap by making policy enforcement automatic and auditable. When classification, lineage, and access controls are integrated, policies become part of the system rather than an external expectation. This shifts governance from aspiration to execution.

But technology alone isn’t enough. Organisations need stewardship, accountability, and a culture that treats governance as part of delivery, not a hurdle to clear. Operationalising policy requires alignment across teams, clarity of ownership, and a commitment to continuous improvement.

The governance gap is not inevitable. It’s a symptom of misalignment. When organisations align policy, technology, and behaviour, governance becomes a strategic enabler rather than a compliance burden.

AI Is Making Us Dumber, Part III: When Models Learn Faster Than Organisations Do

We have reached a point where models can learn faster than organisations can adapt. This creates a dangerous asymmetry: the technology evolves, but the governance, culture, and literacy lag behind. The result is a widening gap between capability and control. Organisations deploy increasingly powerful models without fully understanding their behaviour, limitations, or risks.

This gap is not caused by technology, it is caused by organisational inertia. Many teams still rely on outdated governance processes that cannot keep pace with continuous learning systems. Policies are static, reviews are infrequent, and oversight is reactive. Meanwhile, models evolve with every new dataset, every retraining cycle, and every shift in user behaviour.

The solution is not to slow the models  but it is to accelerate organisational learning. Governance must become continuous, adaptive, and embedded into operational workflows. This means real‑time monitoring, dynamic policies, and stewardship that evolves alongside the data. It also means investing in literacy so that teams understand not just how to use AI, but how to question it.

When organisations learn as fast as their models, AI becomes a strategic advantage. When they don’t, AI becomes a liability. The choice is not technological, it is cultural.

I spoke on the topic at the  Data Toboggan Winter Edition in a session entitled 'Data Literacy: The Human Advantage in an AI World.'