After nearly five years in Solution Sales and now serving in a technical capacity as a Principal Solution Engineer for Data Platform, I’m often asked: Why is Azure Databricks such a strategic priority for Microsoft? More recently, another question has emerged: Does it compete with Microsoft Fabric?

Let me be clear: Azure Databricks is a first-party service in Microsoft’s analytics portfolio. It’s not just a partner product—it’s a service with a great integration and interoperability with the Microsoft ecosystem, and we treat it as an Azure offering. Ultimately, Microsoft's customers have two incredible platform options on Azure to build their lakehouse and advanced analytics solutions: Microsoft Fabric and Azure Databricks. Today, I will focus only on Azure Databricks.

Why Azure Databricks Is First-Party—and Why That Matters

Microsoft treats Azure Databricks as a first-party solution, not just a partner product. This means it receives the same level of attention, integration, and support as any other Microsoft-native service.

For customers, this translates into a unified support experience. If you’re running Azure and Databricks under a joint support contract, you don’t need to navigate separate channels—Microsoft covers Azure Databricks just like any other product, with the added advantage of Databricks engineers collaborating directly with Microsoft support teams. This ensures faster resolutions, deeper expertise, and a seamless experience.

𝗢𝗻 𝗝𝘂𝗻𝗲 12, during Databricks Data + AI Summit, Databricks and Microsoft announced an extension of their strategic partnership for Azure Databricks, building on the collaboration that began in 2018. The announcement highlights recent product innovations, including native integrations between Azure Databricks, Azure AI Foundry, and Microsoft Power Platform, as well as the upcoming release of SAP Databricks on Azure.

According to Ali Ghodsi, Co-founder and CEO of Databricks. “𝘛𝘩𝘪𝘴 𝘦𝘹𝘵𝘦𝘯𝘥𝘦𝘥 𝘱𝘢𝘳𝘵𝘯𝘦𝘳𝘴𝘩𝘪𝘱 𝘸𝘪𝘵𝘩 𝘔𝘪𝘤𝘳𝘰𝘴𝘰𝘧𝘵 𝘴𝘩𝘰𝘸𝘴 𝘰𝘶𝘳 𝘭𝘰𝘯𝘨-𝘵𝘦𝘳𝘮 𝘫𝘰𝘪𝘯𝘵 𝘤𝘰𝘮𝘮𝘪𝘵𝘮𝘦𝘯𝘵 𝘵𝘰 𝘩𝘦𝘭𝘱𝘪𝘯𝘨 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯𝘴 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘦 𝘧𝘢𝘴𝘵𝘦𝘳 𝘸𝘪𝘵𝘩 𝘢 𝘶𝘯𝘪𝘧𝘪𝘦𝘥, 𝘰𝘱𝘦𝘯, 𝘢𝘯𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘦𝘥 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘧𝘰𝘳 𝘥𝘢𝘵𝘢 𝘢𝘯𝘥 𝘈𝘐 𝘰𝘯 𝘈𝘻𝘶𝘳𝘦 𝘋𝘢𝘵𝘢𝘣𝘳𝘪𝘤𝘬𝘴. 𝘛𝘰𝘨𝘦𝘵𝘩𝘦𝘳, 𝘸𝘦’𝘳𝘦 𝘭𝘢𝘺𝘪𝘯𝘨 𝘵𝘩𝘦 𝘧𝘰𝘶𝘯𝘥𝘢𝘵𝘪𝘰𝘯 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘯𝘦𝘹𝘵 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘈𝘐-𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦𝘴.”

From my experience working with hundreds of customers across Latin America, this partnership proves its strength. For further evidence, check out these articles I wrote in 2021 and 2023:

• Powering the Future of Analytics: Microsoft and Databricks (June 9, 2023) https://www.linkedin.com/pulse/powering-future-analytics-microsoft-databricks-pablo-junco-boquer

• Innovate Faster by Migrating from Hadoop to Azure Databricks (March 12, 2021) https://www.linkedin.com/pulse/innovate-faster-migrating-from-hadoop-azure-pablo-junco-boquer/?trackingId=Za7KhPrWTOSHQRdk%2Bx3jIg%3D%3D

From a go-to-market (GTM) perspective, Microsoft Sellers, Cloud & AI specialists and Solution Engineers are compensated equally whether a customer chooses Microsoft Fabric or Azure Databricks. This alignment removes any internal competition and allows us to focus on recommending the best solution for the customer’s needs.

Microsoft sellers are compensated equally whether a customer chooses Microsoft Fabric or Azure Databricks.

As a side note, we’ve standardized on Delta Parquet as the foundation for Microsoft Fabric’s lakehouse architecture—the same format used by Azure Databricks. This enables customers to move data seamlessly between platforms without reshaping their data—it’s interoperability by design. That said, thousands of large enterprises use Microsoft Fabric alone, and many more use Microsoft Fabric with Azure Databricks beyond Data Factory and Power BI capabilities. In the end, it’s entirely the customer’s choice.

Why Databricks Runs Better on Azure

Azure is the best cloud for running Databricks workloads. Period.

Okay! For those of us who live and breathe data, the proof is in the numbers. According to a new third-party benchmark from Principled Technologies , Azure Databricks delivers 21% faster single query execution vs Databricks cluster on Amazon Web Services (AWS) and saves over 9 minutes on concurrent queries.

These results aren’t just statistics—they translate into more efficient operations and the ability to scale without compromise. Whether you’re running analytics, AI workloads, or big data pipelines, Azure Databricks provides unmatched performance and reliability, which is why it’s a strategic priority for Microsoft and why customers trust it to power their most critical data initiatives.

From native integration with Microsoft Entra ID (formerly Azure AD) and Microsoft Purview to optimized networking, compute, and storage, Azure provides the most secure, scalable, and performant environment for Databricks. Features like Lakeflow, Unity Catalog, and Declarative Pipelines are tightly coupled with Azure services, enabling seamless orchestration, governance, and automation.

But it’s not just about infrastructure—it’s about the better together approach. Azure and Databricks jointly deliver a unified experience that simplifies architecture, accelerates innovation, and reduces operational overhead.

This synergy is already delivering results for customers across the globe. In Latin America, Banco Bradesco built a real-time customer data platform using Databricks and Lakeflow Declarative Pipelines, achieving a 95% reduction in processing time and doubling conversion rates. Similarly, Credicorp was recently highlighted by the Databricks CEO for its success in modernizing its data estate with Azure Databricks.

Want to learn more about end-to-end Data Intelligence architecture with Azure Databricks? I invite you to explore Databricks’ brand-new Architecture Center—packed with blueprints to build and scale your organization’s most critical Data & AI initiatives. Here, you’ll find their end-to-end Data Intelligence reference architecture with Azure Databricks.

Discover more here: https://lnkd.in/dBwrnVVP

Integration Points Across Microsoft’s Portfolio

Let me break down how Azure Databricks connects with the broader Microsoft ecosystem:

▪️ Microsoft Fabric

Fabric and Databricks share the same lakehouse foundation. With OneLake shortcuts and Mirroring, customers can reuse data across platforms without duplication. Whether you prefer medallion or data mesh architectures, Fabric and Databricks work together to eliminate silos.

Take, for example, Microsoft Fabric Mirroring for Azure Databricks Unity Catalog, which is now generally available for all our joint customers. For me, this is a game-changing capability, enabling organizations to analyze their entire multi-cloud and on-premises data estate—all from a single mirrored dataset in OneLake, with no data movement or duplication. With Azure Databricks and Microsoft Fabric—two of the most powerful engines in the Azure data ecosystem—now working seamlessly together, customers are already seeing major benefits:

I also invite you to read one of my previous article, where I explored how AI-based Multi-Agent Systems are reshaping business workflows—and how Microsoft Fabric is uniquely positioned to accelerate this journey. For me, this represents a mechanism for organizations to maximize their existing data estate investments—whether in data lakes, warehouses, or databases—without paying for duplicating data or building complex, time-consuming ETL pipelines, thanks to Microsoft Fabric’s Mirroring. In the article, I focus on practical examples of integrations with Azure Databricks and Oracle.

▪️ Power BI

Power BI (now part of Microsoft Fabric) was one of the first integrations with Databricks SQL. It’s a two-click setup to connect, and customers can choose to visualize data in Power BI or within Databricks itself.

Today, integrating Azure Databricks with Power BI provides a streamlined way to keep datasets and reports up to date by connecting data pipelines directly to analytics workflows. This can be done in two ways:

1. Publishing Databricks tables stored in Unity Catalog directly into Power BI workspaces, with authentication handled via Microsoft Entra ID or PAT and flexible options for DirectQuery or Import modes.

2. Automating refreshes of Power BI semantic models through Databricks workflows, using Unity Catalog external connections to trigger updates whenever source data changes.

Together, these approaches enable governed, automated, and real-time insights across both platforms.

▪️ Azure AI Foundry

Azure AI Foundry now provides native integration with Azure Databricks, enabling the development of AI agents that can consume and act on real-time enterprise data.

The integration leverages Databricks AI/BI Genie for natural language querying, with governance and access control enforced through Unity Catalog. This allows agents to retrieve domain-specific insights while maintaining compliance and security. Additionally, the integration supports multi-agent workflows, allowing AI agents to coordinate on tasks such as advanced analytics, contextual content generation, and streaming intelligence across business scenarios. You can learn more here.

▪️ Microsoft Power Platform

At the summit, I had the chance to see the live Anavi Nahar's announcement of the Public Preview for the new Azure Databricks connector in Power Platform. Developed by Databricks, this connector enables seamless integration between Azure Databricks and Microsoft’s low-code ecosystem—including Power Apps, Power Automate, and Copilot Studio.

In my view, it’s a game changer: bridging advanced analytics with business process automation to deliver secure, governed, real-time data access—without the complexity of custom connectors or the risks of data duplication. This capability is available exclusively for Azure Databricks customers. You can learn more here.

▪️ Microsoft Purview

Unified governance is critical. Databricks integrates with Microsoft Purview via Unity Catalog, providing metadata management, lineage tracking, and access control across the data estate.

Azure Purview provides two integration options with Azure Databricks to strengthen data governance and lineage visibility:

1. Hive Metastore Integration – Microsoft Purview can scan the Databricks Hive metastore to register technical metadata (workspaces, databases, tables, views) and capture static lineage between tables and views. It also maps external table relationships to ADLS Gen2 or Blob Storage, helping organizations centralize governance for legacy Hive-based workloads.

2. Unity Catalog Integration – With Unity Catalog, MicrosoftPurview enables a more modern governance approach. It extracts detailed metadata across metastores, catalogs, schemas, tables, and views, while also capturing dynamic lineage down to the column level during notebook runs. This provides greater visibility into how data flows across Databricks workloads, making it easier to enforce compliance and manage end-to-end data estates.

Key Takeaway: Hive Metastore support ensures continuity for existing deployments, while Unity Catalog integration provides a future-ready governance model with deeper lineage and security controls. Organizations should evaluate both options based on their current architecture, compliance needs, and Databricks adoption roadmap.

External Integration Capabilities by Databricks

Databricks doesn’t just integrate with Microsoft’s core analytics stack—it also connects with a wide range of external systems and services:

• Azure Synapse Analytics: High-performance connectors enable fast data transfer between Databricks and Synapse, supporting streaming and batch workloads.

• Azure Cosmos DB: Advanced integration patterns allow real-time analytics and data synchronization between CosmosDB and Databricks.

• SQL Server: Lakeflow Connect supports ingestion from SQL Server, including on-premises and cloud-hosted instances.

• Microsoft SharePoint & Dynamics 365: Lakeflow Connect includes pre-built connectors for ingesting data from enterprise applications like SharePoint and Dynamics 365.

• Azure IoT Hub: Databricks can ingest and process sensor and telemetry data from IoT Hub, enabling real-time analytics for industrial and smart applications.

These integrations are powered by Lakeflow Connect, Declarative Pipelines, and Lakeflow Designer, which offer no-code and low-code options for building production-grade ETL pipelines with built-in governance and AI productivity.

Click here to learn more about Azure Databricks' managed connectors in Lakeflow Connect.

Why Microsoft Sellers Recommend Azure Databricks

There’s no internal competition between Fabric and Databricks. Sellers are incentivized to recommend the best solution for the customer’s scenario, not a specific product. Many customers use both platforms, thanks to interoperability features like Mirroring, OneLake, and Power BI integration.

Beyond economic alignment, Microsoft employees in the field recognize the additional benefits Azure Databricks brings to our joint customers. Databricks often increases the number of technical and subject matter experts serving the customer, which means more technical intensity, more innovation, and better issue anticipation. Together, Microsoft and Databricks share best practices for implementation, deployment, operations, and even cost/value optimization.

Databricks also enriches Microsoft’s enablement catalog by offering incredible training programs and certifications, helping our teams and customers stay ahead of the curve.

Final Thoughts

Azure Databricks is not just a tool—it’s a strategic pillar in Microsoft’s data and AI vision. It empowers customers to build scalable, intelligent solutions while staying fully integrated with the Microsoft ecosystem.

If you're a customer evaluating your analytics strategy, know this: you don’t have to choose between Fabric and Databricks. You can have both.

Let’s build the future of data together.