Synapse Link to Fabric Link: Modernising Dataverse Analytics

Azure Synapse Link for Dataverse has given many Dynamics 365 CRM implementations a powerful near real‑time bridge into Azure analytics, but it also introduces operational overhead: storage accounts to manage, security boundaries to configure, Azure Synapse Pipelines to maintain, and data copies to govern. Link to Microsoft Fabric offers a fundamentally different model: projecting Dataverse data directly into OneLake as an optimised Delta Lakehouse, SQL endpoint, and DirectLake‑ready Power BI dataset, reducing ETL complexity but shifting cost and capacity considerations back to Dataverse storage at a premium rate—a trade‑off that may not suit all use cases.

This intermediate‑level session walks through a real‑world Dynamics 365 CRM and Azure Synapse Link implementation and then presents a practical migration blueprint to Link to Microsoft Fabric, based on current design and proof‑of‑concept work. It is aimed at data engineers, BI developers, and Power Platform/SQL professionals who need to modernise their Dataverse analytics without blindly accepting "magic button" marketing.

Instead of a polished success story, you will see an honest "work in progress" view: the Synapse Link architecture, the specific pain points triggering the move, and a validated "future state" design informed by 2025 platform updates. Live demos (from a lab environment) will show the end‑to‑end flow: configuring the Fabric Link, inspecting the auto‑generated Lakehouse and SQL endpoint, demonstrating how Azure Synapse Pipelines would be repointed to Fabric, repointing a Power BI report to DirectLake, and highlighting how schema changes and permission misconfiguration can break existing reports—and how to prevent them.

1. Understand when to stay on Azure Synapse Link vs. migrate to Fabric Link based on architectural, cost, and governance trade-offs specific to Dynamics 365 CRM. 2. Learn a practical, step-by-step migration blueprint for running both links side-by-side and safely repointing Power BI reports, SQL queries, and Azure Synapse Pipelines. 3. Avoid critical pitfalls (table limits, schema evolution, security model changes, storage costs) with a validated checklist from real proof-of-concept work.