The question "should we use Synapse or Databricks?" comes up in every Azure data platform conversation, and most answers are either "it depends" (useless) or heavily influenced by which Microsoft rep you last spoke to. Let me give you the structural answer: Synapse and Databricks target different primary use cases, have meaningfully different Spark implementations, and the "one platform does everything" framing from both vendors is misleading. Understanding the actual architectural differences helps you make the choice rationally rather than by vibes.
Azure Synapse Analytics: The Unified Vision
Synapse's pitch is convergence: a single workspace where you can run SQL analytics (Dedicated SQL Pool — the former Azure SQL Data Warehouse), ad-hoc SQL on files (Serverless SQL Pool), Spark notebooks, data pipelines (integrated ADF-equivalent), and connect Power BI — all in one portal with unified security. The OneLake precursor pattern: a single ADLS Gen2 workspace storage container shared across all compute types.
Synapse Dedicated SQL Pool
This is the MPP warehouse — the workload Synapse was originally built for. It uses a Massively Parallel Processing architecture with a control node and compute nodes, columnar storage (similar to Redshift), and has specific design requirements (distribution keys, statistics, clustered columnstore indexes). For pure SQL analytics workloads at terabyte-to-petabyte scale that don't need Python or ML, Dedicated SQL Pool is competitive with Redshift and Snowflake. Cost model: DWU (Data Warehouse Units) per hour, pauseable when idle.
Synapse Serverless SQL Pool
Query files in ADLS Gen2 or Delta Lake tables using standard T-SQL — no cluster to manage, pay per TB scanned (like BigQuery). This is genuinely useful for exploratory analytics over data lake files without Spark overhead. Limitations: no write operations to regular tables, limited Delta Lake DML support, performance varies significantly based on file size and partitioning.
Synapse Spark
Spark pools in Synapse run Apache Spark (currently 3.x) with a Synapse-specific connector layer. They work, but they lag behind Databricks Runtime in several important dimensions: no Photon engine, older Delta Lake support, no MLflow/Unity Catalog integration, and slower adoption of Spark upstream improvements. If your Spark workloads are simple ETL, Synapse Spark is adequate. If you're doing complex Spark optimization, DeltaLake MERGE operations, or ML, the gap with Databricks becomes painful.
Azure Databricks: Spark-First, ML-Native
Databricks on Azure runs on Azure infrastructure but with the full Databricks control plane — the same product you'd get on AWS or GCP, with Azure-specific connectors (ADLS Gen2, Azure SQL, Synapse Serverless). The differentiation vs Synapse Spark:
- Photon Engine: Native C++ vectorized execution engine for Spark SQL, providing 2–5x query speedup for common analytics workloads
- Delta Lake (latest): Databricks ships and maintains Delta Lake, so their runtime has Delta features months before they appear in Apache Spark releases
- Unity Catalog: Cross-workspace governance with column-level security, lineage, Delta Sharing — significantly more mature than Synapse's security model
- MLflow + Feature Store: Best-in-class ML lifecycle tooling built into the platform
- Job compute vs interactive compute: Separate billing tiers — much cheaper for scheduled batch jobs
The Actual Decision Framework
| Your Situation | Lean Synapse | Lean Databricks |
|---|---|---|
| Primary workload is SQL analytics at scale | ✅ Dedicated SQL Pool | — |
| Need Python/ML as a first-class citizen | — | ✅ |
| Complex Delta Lake operations (MERGE, time travel, schema evolution) | — | ✅ Photon + latest Delta |
| Already deep in Microsoft stack (Purview, Power BI Premium) | ✅ Better native integration | — |
| Need enterprise ML governance (Feature Store, Unity Catalog) | — | ✅ |
| Budget: minimize vendor lock-in risk | — | ✅ Open formats + Delta |
| Already have ADF pipelines + Synapse investment | ✅ Lower migration cost | — |
| Serverless ad-hoc SQL on data lake files | ✅ Serverless SQL Pool | — |
The hybrid pattern most large Azure shops end up running: Azure Synapse (Dedicated SQL Pool) for the SQL analytics/BI workload where the MPP architecture is a good fit, ADF for data movement and orchestration, and Databricks for the Spark-heavy ETL and all ML/AI work. Synapse Spark sits unused or lightly used because Databricks is better for serious Spark work. This isn't elegant, but it reflects what each product actually does well.
Cost Reality Check
The pricing comparison is complex because the products bill differently. A rough equivalence for a typical analytics workload:
- Synapse Dedicated SQL Pool: DW500c (~$7.20/hr when running). You pause it when idle. For a warehouse running 8 hours/day, 20 days/month: $7.20 × 8 × 20 = $1,152/month. Plus storage.
- Databricks (Jobs Compute): A 4-node Standard_DS3_v2 cluster running daily batch jobs for 4 hours/day: ~$0.15/DBU × ~20 DBUs × 4 hrs × 30 days ≈ $360/month in DBU costs + ~$250/month in VM costs = ~$610/month for batch-only workloads.
- Databricks (All-Purpose): Add interactive clusters for data science/engineering exploration and the monthly bill can easily reach $3,000–8,000 for a team.
The key Databricks cost variable: the ratio of Jobs Compute ($0.10–0.15/DBU) to All-Purpose Compute ($0.30–0.55/DBU) usage. Teams that discipline their workloads onto Jobs Compute get a very different bill than teams that leave All-Purpose clusters running. This is where governance matters most.
Microsoft Fabric Changes the Calculus
With Microsoft Fabric's GA in November 2023, the question increasingly becomes "Fabric vs Databricks" rather than "Synapse vs Databricks." Fabric effectively replaces Synapse for new Azure projects — it uses OneLake (Delta on ADLS Gen2) as the unified storage layer, includes Synapse-equivalent SQL analytics, has its own Spark environment, and integrates Power BI natively. If you're starting a new Azure data platform today and not already invested in Databricks, Fabric is the right first conversation to have.