Both Databricks and Azure Synapse promise a unified analytics experience. But they differ significantly in architecture, cost model, and ecosystem fit. This comparison helps you decide.
| Aspect | Databricks | Azure Synapse |
|---|---|---|
| Core engine | Apache Spark (Photon-optimised) | Spark + dedicated SQL pool |
| Table format | Delta Lake (native) | Delta Lake or Parquet |
| Governance | Unity Catalog | Purview integration |
| Serverless | SQL + Jobs + Notebooks | SQL on-demand + Spark |
| ML / AI | MLflow, Feature Store, Model Serving | Azure ML integration |
| Real-time | Structured Streaming | Synapse Link, Stream Analytics |
Databricks’ Photon engine consistently outperforms Synapse Spark pools on TPC-DS benchmarks — often by 2-5x. For SQL workloads, Synapse dedicated SQL pools can be competitive for traditional DW patterns, but Databricks SQL warehouses have closed the gap with serverless SQL.
Direct cost comparison is nuanced because the billing models differ:
In our experience across client engagements, Databricks tends to be more cost-effective for heavy Spark workloads and ML, while Synapse dedicated SQL can be cheaper for pure SQL DW workloads with predictable patterns — provided you manage pause/resume discipline.
Microsoft Fabric is increasingly replacing Synapse for new projects. If you are choosing between Synapse and Databricks today, also evaluate Fabric as a third option — especially if Power BI is central to your analytics stack.