The bottom line
Fabric wins on unified experience, Direct Lake for Power BI, and the Fabric Data Agent. Synapse wins on Dedicated SQL Pool performance for large-scale structured workloads and existing enterprise contract arrangements. For new deployments, Fabric is almost always the right start.
In This Article
The Microsoft Direction of Travel
Microsoft has been explicit: Fabric is the future of the analytics platform, and Synapse Analytics is in maintenance mode for existing customers. New feature investment is going into Fabric. Synapse Pipelines are being converged into Fabric Data Factory. The Synapse Spark runtime is the same engine that runs inside Fabric notebooks. Microsoft will not end-of-life Synapse imminently — there are too many enterprise customers on multi-year contracts — but if you are starting a new data platform project today, choosing Synapse over Fabric requires a specific justification.
The honest version of that justification is usually one of three things: your organisation has a large existing Synapse Dedicated SQL Pool workload that has already been tuned and is performing well; you are in a regulated environment where your compliance team has approved Synapse and is not yet ready to certify Fabric; or your Azure enterprise agreement has committed Synapse capacity that is not easily transferable.
Compute: Spark and SQL Compared
Fabric Spark and Synapse Spark run the same underlying Apache Spark engine, but the operational experience differs. Fabric Spark notebooks have faster startup times (around 20-30 seconds versus 2-3 minutes for Synapse Spark pool cold start), a better collaborative notebook experience, and tighter integration with OneLake storage. For data engineering workloads — pipeline development, transformation notebooks, ML training — Fabric Spark is a materially better experience.
Synapse Dedicated SQL Pool remains ahead of Fabric Warehouse for large-scale structured analytics workloads. A Synapse Dedicated SQL Pool provisioned at DW2000c handles 200+ concurrent queries and petabyte-scale star schema joins with consistent, low-latency performance. Fabric Warehouse is improving rapidly but does not yet match Dedicated SQL Pool on concurrent query throughput for the largest workloads.
Storage Model: OneLake vs ADLS
The storage architecture is where Fabric and Synapse differ most fundamentally. Synapse stores its own data in Azure Data Lake Storage Gen2 — a conventional ADLS account your team manages, with all the access control, lifecycle management, and cost management that implies. Fabric uses OneLake — a single, Microsoft-managed data lake where all Fabric artefacts share one storage namespace.
OneLake simplifies operations significantly: no storage account key rotation, no RBAC role assignment on the storage account itself, no lifecycle management policies to configure, and no cost surprises from accidental data retention. The trade-off is less control — if OneLake has an outage, you cannot fail over to a secondary storage region independently of the Fabric service.
Power BI Integration
This is where Fabric's advantage over Synapse is clearest. Power BI Direct Lake mode is exclusive to Fabric — it reads Delta tables from OneLake directly into the Power BI in-memory column store without import or DirectQuery overhead. The performance profile is import-speed query execution on data that is current to the last pipeline run, without the storage cost of maintaining a separate import dataset.
On Synapse, Power BI connects via DirectQuery against the Synapse Serverless SQL endpoint or the Dedicated SQL Pool. DirectQuery performance is workload-dependent — simple queries are fast, complex DAX measures generating multi-join SQL are slow. For Power BI-heavy deployments — and most mid-market manufacturing and FMCG organisations are Power BI-heavy — Fabric's Direct Lake mode is a decisive advantage.
Direct Lake mode alone justifies the migration from Synapse to Fabric for any organisation with more than 30 Power BI users. The performance difference on complex dashboards is the difference between a dashboard that loads in 2 seconds and one that loads in 12.
The Migration Decision
The migration from Synapse to Fabric is not technically complex for most workloads. Synapse Pipelines are functionally equivalent to Fabric Data Factory pipelines. Synapse Spark notebooks copy to Fabric notebooks with minor syntax changes. The Synapse Serverless SQL endpoint has a direct equivalent in the Fabric Lakehouse SQL analytics endpoint. The data in ADLS can be accessed from Fabric via OneLake Shortcuts without physical movement.
The cases where migration is more complex: Synapse Dedicated SQL Pool (no direct equivalent in Fabric — the closest is Fabric Warehouse with different performance characteristics); Synapse integration with Azure Purview for data governance (Fabric uses Microsoft Purview natively but the configuration differs). For these components, plan for a 4-8 week migration effort per workload.
For most new data platform projects in manufacturing, FMCG, or supply chain, Fabric is the right start. For existing Synapse customers, the migration case is strongest when you have Power BI-heavy reporting workloads and want the Direct Lake performance improvement. If you want to assess your specific Synapse setup and understand the migration scope and benefit, I am happy to work through it.
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