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The Databricks-Fabric Connection: Paths, Pitfalls, and Possibilities
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In this deep-dive episode, we unpack the technical and strategic implications of integrating Databricks with Microsoft Fabric. As enterprises increasingly operate across hybrid data ecosystems, ensuring seamless interoperability between platforms is critical. This episode covers the full landscape—OneLake mounting techniques, Delta Sharing, Unity Catalog mirroring, and Direct Lake integration with Power BI.
You’ll learn:
- How to securely mount and access OneLake and ADLS Gen2 from Databricks
- When to use service principals vs. credential passthrough
- What Delta Sharing enables between Databricks and Fabric, and its governance model
- How Lakehouse Federation supports federated querying across systems
- Why Unity Catalog mirroring to Fabric Lakehouse is a game-changer—and its limitations
- How Direct Lake mode in Power BI reshapes performance and semantic model design
- Practical guidance on security, schema consistency, and performance trade-offs
Whether you're a data architect, platform engineer, or analytics leader, this episode equips you with the insights to design and optimize cross-platform data architectures that scale.
Thank you for tuning in to "Analyze Happy: Crafting Your Data Estate"!
We hope you enjoyed today’s deep dive. If you found this episode helpful, don’t forget to subscribe for more insights on building modern data estates with Microsoft technologies like Fabric, Azure Databricks, and Power Platform.
Connect with Us:
- Have a question or topic you’d like us to cover? Reach out on linkedin.com/company/dataqubi or [email protected]
- Visit our website at www.dataqubi.com or episode resources, show notes, and additional tips on data governance, AI transformation, and best practices.
Stay Ahead:
Check out the Microsoft Learn portal for free training on Azure IoT, Fabric, and more, or explore the Azure Databricks community for the latest updates. Let’s keep crafting data solutions that fit your organization’s culture and tech landscape—happy analyzing until next time!
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