Microsoft brings transactional databases to Fabric to boost AI agents

For years, enterprise companies have been plagued by data silos separating transactional systems from analytical tools—a divide that has hampered AI applications, slowed real-time decision-making, and driven up costs with complex integrations. Today at its Ignite conference, Microsoft announced a major step toward breaking this cycle.

The tech giant revealed that Azure SQL, its flagship transactional database, is now integrated into Fabric, Microsoft’s unified data platform. This integration allows enterprises to combine real-time operational and other historical data into a single, AI-ready data later called OneLake. 

This announcement represents a critical evolution of Microsoft Fabric, its end-to-end data platform, which also includes new capabilities like real-time intelligence and the general availability of the OneLake catalog (see our full coverage of the Microsoft Ignite data announcements here). Together, these updates aim to address the growing demand for accessible, high-quality data in enterprise AI workflows.

Until now, companies have struggled to connect disparate data systems, relying on patchwork solutions to support AI applications. The urgency has only increased with the rise of AI agents—software tools capable of performing complex reasoning autonomously. These agents require instantaneous access to live and historical data to function effectively, a demand Microsoft aims to meet with Fabric.

And with AI agents becoming one one of the hottest trends for enterprise companies next year, Microsoft is pushing to lead here. See our separate coverage about how Microsoft is ahead in this race, and no one else is close.

The integration of Azure SQL is just the beginning of this integration of transactional data. Microsoft plans to extend support to other key transactional databases, including Cosmos DB, its NoSQL document database widely used in AI applications, and PostgreSQL, the popular open-source relational database. While timelines for these integrations remain unspecified, this marks a significant milestone in Microsoft’s effort to create a truly unified data platform. 

Microsoft also said it plans to integrate with popular open source transactional databases, including MongoDB, and Cassandra, but it’s unlikely Microsoft will prioritize integration with competing proprietary transactional databases like Couchbase and Google’s Bigtable.

The power of unified data integration

Arun Ulag, corporate vice president of Azure Data, emphasized in an interview that integrating transactional databases like Cosmos DB into Fabric is critical for enabling next-generation AI applications. For example, OpenAI’s ChatGPT—the fastest-growing consumer AI product in history—relies on Cosmos DB to power its conversations, context, and memory, managing billions of transactions daily.

As AI agents evolve to handle complex tasks like e-commerce transactions, the demand for real-time access to transactional databases will only grow. These agents rely on advanced techniques like vector search, which retrieves data based on semantic meaning rather than exact matches, to answer user queries effectively—such as recommending a specific book.

“You don’t have the time to…go run your RAG model somewhere else,” Ulag said, referencing retrieval-augmented generation models that combine real-time and historical data. “It has to be just built into the database itself.”

By unifying operational and analytical capabilities, Fabric allows businesses to build AI applications that seamlessly leverage live transactional data, structured analytics, and unstructured insights.

Key advancements include:

• Real-time intelligence: Built-in vector search and retrieval-augmented generation (RAG) capabilities simplify AI application development, reducing latency and improving accuracy.
• Unified data governance: OneLake provides a centralized, multi-cloud data layer that ensures interoperability, compliance, and easier collaboration.
• Seamless code generation: Copilot in Fabric can automatically translate natural language queries into SQL, allowing developers to get inline code suggestions,  real-time explanations and fixes.

AI Skills: simplifying AI agent app development

One of the most dynamic announcements in Fabric is the introduction of AI Skills, a capability that enables enterprises to interact with any data – wherever it resides –  through natural language. They connect to Copilot Studio, so you can build AI agents that easily query this data across multiple systems, from transactional logs to semantic models.

Ulag said that if he had to pick one announcement that excites him the most, it would be AI Skills. With AI Skills, business users can simply point to any dataset — be it from any cloud, structured, or unstructured – and begin asking questions about that data, whether through natural language, SQL queries, Power BI business definitions, or real-time intelligence engines, he said. 

For example, a user could use AI Skills to identify trends in sales data stored across multiple systems or to generate instant insights from IoT telemetry logs. By bridging the gap between business users and technical systems, AI Skills simplifies the development of AI agents and democratizes data access across organizations.

As of today, AI Skills can connect with lakehouse and data warehouse tables, mirrored DB and shortcut data, and now semantic models and Eventhouse KQL databases. Support for unstructured data is “coming soon,” the company said. 

Differentiation in a crowded market

Microsoft faces fierce competition from players like Databricks and Snowflake on the data platform front, as well as AWS and Google Cloud in the broader cloud ecosystem—all of which are working on integrating transactional and analytical databases. However, Microsoft’s approach with Fabric is beginning to carve out a unique position.

By leveraging a unified SaaS model, seamless Azure ecosystem integration, and a commitment to open data formats, Microsoft eliminates many of the data complexities that have plagued enterprise data systems. Additionally, tools like Copilot Studio for building AI agents and Fabric’s deep integration across multi-cloud environments give it an edge (see my separate analysis [LINK] of Microsoft’s positioning around AI agents, which also appears to be industry-leading).

Microsoft’s ability to embed AI capabilities directly into its unified data environment “could provide a better experience for developers and data scientists,” said Robert Kramer, vice president at research firm Moor Insights, underscoring how Fabric’s design simplifies workflows and accelerates AI-driven innovation.

Key differentiators include:

• Unified SaaS model: Fabric eliminates the need to manage multiple services, offering enterprises a single, cohesive platform that reduces complexity and operational overhead.
• Multi-cloud support: Unlike some competitors, Fabric integrates with AWS, Google Cloud, and on-premises systems, enabling organizations to work seamlessly across diverse data environments.
• AI-optimized workflows: Built-in support for vector similarity search and retrieval-augmented generation (RAG) streamlines the creation of intelligent applications, cutting development time and improving performance.

Microsoft’s strategy to unify and simplify the enterprise data stack not only meets the demands of today’s AI-centric workloads but also sets a high bar for competitors in the rapidly evolving data platform market.

The road ahead: where Fabric fits in the AI ecosystem

The integration of transactional databases into Fabric marks a significant milestone, but it also reflects a broader shift across the enterprise data landscape: the race toward seamless interoperability. With AI agents becoming a cornerstone of enterprise strategy, the ability to unify disparate systems into a cohesive architecture is no longer optional—it’s essential.

However, Arun Ulag, corporate vice president of Azure Data, acknowledged the challenges that come with operating at Microsoft’s scale. While the company has taken major strides with Fabric, the fast-moving nature of the industry demands constant innovation and adaptability.

“A lot of these patterns are new,” Ulag explained, describing the challenges of designing for a diverse set of use cases across industries. “Some of these patterns will work. Some of them will not, and we’ll only know as customers try them at scale…The way it’s used in automotive may be very, very different from the way it’s used in healthcare,” he added, emphasizing the role of external forces like government regulations in shaping future development.

As Microsoft continues to refine Fabric, the company is positioning itself as a leader in the shift to unified, AI-ready data architectures. But with competitors also racing to meet the demands of enterprise AI, the journey ahead will require constant evolution, rapid learning, and a focus on delivering value at scale.

For more insights into the announcements and Arun Ulag’s perspective, watch our full video interview above.