Overview
As organizations scale their AI strategies, choosing between a small language model (SLM) and a large language model (LLM) becomes as much a business decision as a technical one. SLMs are typically valued for their efficiency, lower cost, and ability to perform targeted tasks well, while LLMs are better suited for broader reasoning, deeper context handling, and more advanced generative capabilities.
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Microsoft Fabric continues to evolve beyond a unified analytics platform and into an agent-driven system that actively helps users understand data and operate systems. Two of the most important building blocks of this direction are Data Agents and Operations Agents. While both leverage AI, they serve very different purposes. One focuses on understanding data, and the other focuses on acting on real-time conditions. Together, they represent Microsoft’s shift toward embedding intelligence directly into analytics and operations rather than layering it on afterward.
Overview
As Microsoft Fabric environments mature and become more collaborative, the risk of accidental deletion increases. A data engineer cleaning up a workspace, an analyst removing unused assets, or a contributor misunderstanding dependencies can easily delete the wrong item. Until recently, that deletion was permanent.
Microsoft Fabric now introduces item-level recovery through soft delete, providing a critical safety net for supported Fabric items. This capability complements existing workspace retention and adds fine-grained protection at the item level.
Item recovery allows deleted items to be retained for a configurable period, during which authorized users can restore them or permanently delete them. This feature is currently available in preview and must be explicitly enabled at the tenant level.
Prerequisites and Configuration
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Defense contractors facing CMMC compliance have a fundamental architecture decision to make before they spend a dollar on licensing or migration services. Should you move your entire organization into a Microsoft 365 GCC High tenant, or should you build a CMMC enclave that isolates only the users and systems that handle Controlled Unclassified Information (CUI)? The answer depends on how much of your business actually touches regulated data, what your contracts require, and how much complexity your IT team can realistically manage.
This guide compares both approaches so you can make the right call for your organization's compliance posture, budget, and operations.
Key Insights: What You Need to Know About CMMC Enclave vs. Full GCC High Migration
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For years, the story of enterprise AI has followed a predictable storyline: a promising use case receives approval, a development team builds something impressive, and then the project stalls. The reason is not that the technology failed, but rather that compliance could not keep up. Security reviews, data classification requirements, audit trails, retention policies, and governance work consistently take longer to complete than the actual development.
Microsoft recently announced native integration between Foundry and Purview, and for IT and security leaders who have been watching AI adoption intersect with compliance requirements, this is a development worth paying attention to.
The regulatory countdown that defense contractors have been watching for years is finally over. On November 10, 2025, the Department of Defense began including CMMC 2.0 requirements in contract solicitations - transforming cybersecurity compliance from a policy goal into a binding contractual obligation for anyone in the defense supply chain. If you manufacture components for the DoD, provide engineering services, or operate anywhere in the defense industrial base, CMMC 2.0 compliance now directly determines whether you can bid on and win contracts.
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Microsoft Security Engineering Airlift is an exclusive, invitation‑only event designed to bring together Microsoft’s top engineering teams with a select group of their most strategic and technically advanced partners. A group of my Daymark colleagues and I had the privilege of attending this event for an opportunity to gain unprecedented access to product groups, roadmap insights, and advanced security engineering guidance directly from the teams that build Microsoft’s cybersecurity technologies.
At the core of Microsoft Security’s mission is a powerful motto: “Make the world a safer place for all.” This vision guided the 2026 Microsoft Security Engineering Airlift, held at Microsoft headquarters, as we explored the latest advancements in protecting our digital world. The central theme of the event focused on the dual commitment to security for AI and AI for security, recognizing that artificial intelligence serves both as a powerful tool and a critical asset that must be protected.
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Overview
For many years, organizations commonly relied on on‑premises SQL Server as their enterprise data warehouse. While this approach made sense when cloud platforms were immature, it is no longer necessary, or efficient, in modern analytics architectures. Microsoft Fabric fundamentally changes this model by providing purpose-built, cloud-native analytical engines that eliminate the need to manage traditional data warehouse infrastructure.
Within Fabric, SQL Database and Data Warehouse serve different but complementary roles. Understanding when to use each is critical to building a scalable, governed, and future-proof data platform.
Instead of lifting and shifting legacy SQL Server workloads into the cloud, organizations can now adopt services designed specifically for analytical patterns, elasticity, and tight integration with the broader Fabric ecosystem.
The Shift Away from On-Prem SQL Server as a Data Warehouse
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Overview
The data engineering community has long embraced dbt (Data Build Tool) for its simple, SQL-first approach to building, testing, and orchestrating data transformations. Historically, teams using dbt with Microsoft Fabric had to rely on external compute (local development machines, GitHub Actions, Azure DevOps pipelines, virtual machines, or standalone orchestration platforms like Airflow). These setups worked, but they added friction: managing environments, handling adapters, configuring authentication, and monitoring transformation jobs across disparate systems.
With dbt jobs now running natively inside Microsoft Fabric, that fragmentation disappears. dbt is no longer an external tool bolted onto the modern data estate, it is now a first‑class, integrated capability of Fabric, providing a unified experience for modeling, testing, scheduling, and monitoring transformations directly inside Fabric workspaces.
dbt, Natively Integrated: What’s New
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Overview
Microsoft Fabric has been steadily transforming the data landscape with its unified analytics platform. One of its most powerful components, Fabric Data Agents, enables organizations to automate data workflows, orchestrate pipelines, and manage complex data operations with ease. Now, with these Data Agents being consumed into Microsoft 365 Copilot, the game changes entirely. This integration bridges the gap between enterprise data and everyday productivity tools, making insights more accessible and actionable than ever before.
More than 28,000 customers are already leveraging Microsoft Fabric, including 80% of the Fortune 500. This adoption underscores the trust and scale behind the platform and now, bringing that capability into Copilot amplifies its impact across the enterprise.
Key Points
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