Azure Synapse vs Databricks: Which Should You Use?

The quick answer

Azure Synapse Analytics and Databricks are both enterprise data platforms with significant capability overlap. The short version: Synapse is SQL-first and Azure-native, best for SQL analytics workloads in Microsoft-first organisations; Databricks is Spark-native and ML/AI-focused, best for large-scale data engineering and machine learning workloads that require more than a SQL warehouse can provide.

The decision is complicated by Microsoft Fabric: Microsoft's 2024/2025 platform consolidation integrates Synapse's capabilities into Fabric, which is becoming the Microsoft-first data platform of record for new builds. For organisations already on Synapse, migration to Fabric is Microsoft's stated direction. For organisations choosing between a Microsoft platform and Databricks for new builds, the real decision is Fabric vs Databricks.

What each platform is

### Azure Synapse Analytics

Azure Synapse Analytics is Microsoft's integrated analytics service that combines data warehousing and big data analytics in a single platform. Core components:

**Dedicated SQL Pools** (formerly SQL Data Warehouse) — massively parallel processing (MPP) SQL warehouse. Fixed compute provisioned in advance. High performance for large-scale SQL queries; compute and storage are coupled (scaling up costs are significant). Appropriate for stable, large-scale SQL analytics workloads where consistent performance is required.

**Serverless SQL Pool** — on-demand SQL query execution directly over data in Azure Data Lake Storage Gen2. No provisioned compute. Pay per query. Query Parquet, CSV, or Delta files in ADLS Gen2 without loading them into a warehouse. Useful for ad-hoc exploration and reporting on data lake files.

**Synapse Spark Pools** — managed Apache Spark clusters for big data processing. Spark processing within the Synapse environment. Integration with the Synapse workspace, linked services, and Synapse Data Explorer.

**Synapse Pipelines** — Azure Data Factory's ETL/ELT pipeline capabilities integrated into the Synapse workspace. Drag-and-drop pipeline design, 90+ connectors, integration with Spark and SQL.

The Synapse value proposition is integration: SQL, Spark, pipelines, and BI (Power BI) all within a single Azure workspace, with unified access control via Azure Active Directory.

### Databricks

Databricks is a unified analytics platform built on Apache Spark, founded by the original creators of Spark. Core components:

**Databricks Lakehouse** — the core platform: Delta Lake (open table format with ACID transactions, schema enforcement, time travel), clusters (managed Spark compute), notebooks (Python/SQL/Scala/R), and workflows. The Databricks Lakehouse stores data in open Delta Lake format in cloud object storage (ADLS Gen2 on Azure, S3 on AWS, GCS on GCP).

**Unity Catalog** — Databricks' unified data governance layer. Centralised metadata, fine-grained access control (table, column, row level), data lineage, and audit logging across all Databricks workspaces. The standard for Databricks governance since 2022.

**Databricks SQL** — a serverless SQL compute layer on top of Delta Lake. Warehouse-like SQL query performance on Delta tables. Integrates with Tableau, Power BI, and Looker as a SQL endpoint. Databricks SQL makes Delta Lake tables queryable by SQL analysts without Spark knowledge.

**MLflow** — open-source ML lifecycle management (experiment tracking, model registry, model serving) integrated natively into Databricks. The standard for managing ML/AI model development and deployment on Databricks.

**Databricks Feature Store** — centralised feature repository for ML model training and serving. Features are computed once, stored, and reused across models.

Databricks is multi-cloud (Azure, AWS, GCP) and runs on open formats (Delta Lake, Apache Iceberg support). The platform is designed for data engineers, ML engineers, and data scientists working in Python, Scala, and SQL.

Head-to-head comparison

### SQL analytics

**Synapse Dedicated SQL Pool** delivers excellent performance for traditional data warehousing workloads: large SQL queries over structured data, business intelligence reporting, and structured aggregations. The MPP architecture is mature and well-tuned for SQL analytics. For organisations that primarily need a SQL warehouse — structured data, SQL queries, Power BI reporting — Synapse SQL performs at enterprise scale.

**Databricks SQL** has improved significantly in SQL analytics performance, particularly with the Delta Lake caching and Photon engine improvements in recent releases. For SQL analytics at moderate scale (under 10TB per query), Databricks SQL is competitive with Synapse. At the largest scales (hundreds of terabytes, complex aggregations), dedicated SQL warehouses still outperform.

Advantage: **Synapse** for SQL-first workloads, particularly large-scale structured data warehousing. Databricks SQL is competitive for mixed SQL/ML environments.

### ML and AI workloads

**Databricks** is purpose-built for ML and AI. Spark-native for feature engineering at scale. MLflow for experiment tracking and model registry. The Feature Store for feature management. Databricks Workflows for ML pipeline orchestration. Model serving endpoints for low-latency inference. The platform is designed by ML engineers for ML engineers.

**Synapse** Spark Pools support ML workloads, but the tooling ecosystem is significantly thinner than Databricks. MLflow integration exists but is not native. The ML developer experience on Synapse is noticeably inferior to Databricks for practitioners accustomed to ML-first tooling.

Advantage: **Databricks** — significant, not marginal.

### Data engineering (ELT pipelines)

**Synapse Pipelines** (Azure Data Factory capabilities) provide a code-optional ETL/ELT tool with a visual designer and 90+ connectors. For data engineers building pipelines to load data from business systems into the warehouse, Synapse Pipelines is capable and Azure-native. The visual design approach is accessible to data engineers who prefer GUI-based pipeline development.

**Databricks** handles data engineering through Python or Scala notebooks and the Delta Live Tables (DLT) framework. DLT defines pipelines as SQL or Python code with automatic dependency management, monitoring, and error recovery. The Databricks Workflows feature schedules and orchestrates notebooks and DLT pipelines. For complex, large-scale Spark-based data engineering, Databricks' tooling is more capable than Synapse Pipelines.

Advantage: **Even**, with role-based nuance. For visual, GUI-driven pipeline development, Synapse Pipelines. For code-first, Spark-based data engineering, Databricks.

### Governance and security

**Unity Catalog** (Databricks) is the more modern, comprehensive governance layer. Fine-grained access control (table, column, row level), cross-workspace governance, data lineage, audit logging, and integration with identity providers. Unity Catalog is genuinely strong.

**Microsoft Purview** integrates with Synapse to provide external cataloguing and governance. Synapse itself has Azure Active Directory integration for workspace access control but relies on Purview for lineage and cataloguing. For organisations already invested in the Microsoft Purview + Azure governance stack, Synapse integrates naturally.

Advantage: **Unity Catalog / Databricks** for governance capability. Synapse's advantage is integration with the Microsoft identity and governance ecosystem.

### Azure / Microsoft ecosystem integration

**Synapse** integrates natively with Power BI (direct query from Synapse to Power BI without an intermediate connector), Azure Machine Learning, Azure Cognitive Services, Azure Data Lake Storage Gen2, Azure Active Directory, and Microsoft Purview. For Microsoft-first organisations, Synapse is the natural choice for Azure data platform builds.

**Databricks on Azure** integrates well with ADLS Gen2 (Databricks uses ADLS Gen2 as its default object store on Azure), Azure Active Directory (Unity Catalog integrates with AAD), and Azure DevOps. The integration with Power BI and Azure ML exists but requires more configuration than Synapse's native integration.

Advantage: **Synapse** for Azure ecosystem integration.

### Open table format and multi-cloud portability

**Databricks** built Delta Lake (now an open-source Linux Foundation project). Delta tables are open-format (Parquet-based with a transaction log) and can be read by Spark, Presto, Trino, and other query engines without going through Databricks. Databricks also supports Apache Iceberg natively. For organisations that want to avoid vendor lock-in or maintain portability across cloud platforms, Databricks' open table format commitment is a genuine differentiator.

**Synapse** Dedicated SQL Pool uses a proprietary storage format. Synapse Serverless SQL and Spark Pools can read open formats (Parquet, Delta) in ADLS Gen2, but Synapse's primary SQL compute (Dedicated Pool) is not portable.

Advantage: **Databricks** for open format strategy.

The Microsoft Fabric factor

Microsoft Fabric, launched in 2023 and reaching general availability in 2024, consolidates Power BI, Azure Synapse, Azure Data Factory, and Azure Data Lake Storage into a single SaaS platform built on OneLake. Fabric's data warehouse capability (Fabric Warehouse) replaces Synapse Dedicated SQL Pool for new builds. Synapse Analytics continues to be supported but is not the go-forward platform for Microsoft-native data architectures.

For organisations evaluating new builds in the Microsoft ecosystem, the choice is effectively **Microsoft Fabric vs Databricks**, not Synapse vs Databricks. For organisations on Synapse, Microsoft's migration path is to Fabric. For a detailed breakdown of Fabric, see Microsoft Fabric: what it is, what it replaces, and whether to migrate.

Decision framework

Choose Synapse (or Fabric for new builds) when:

- Your organisation is Microsoft-first — Azure, M365, Power BI, Azure Active Directory

- SQL analytics is the primary use case; ML/AI workloads are secondary or absent

- Power BI is your BI layer and you want native integration without configuration

- Your data engineering team is more comfortable with SQL and GUI-based tools than Python/Spark

- Microsoft licensing economics (E3/E5) make a Microsoft-native stack attractive

Choose Databricks when:

- ML and AI workloads are as important as SQL analytics (or more important)

- Large-scale Spark-based data engineering is core to your data architecture

- Open table format (Delta Lake, Iceberg) and multi-cloud portability are strategic requirements

- Your data engineering and ML teams are Python/Scala-first practitioners

- You are building for multi-cloud or are not committed to Azure exclusively

- Unity Catalog's governance capabilities are important for your requirements

**Common pattern at enterprise scale**: Databricks for data engineering and ML workloads, Synapse (or Fabric) for SQL analytics and Power BI. The two platforms are not mutually exclusive — reading Delta tables in ADLS Gen2 via Synapse Serverless SQL (or Fabric Direct Lake) allows SQL analysts and Power BI to consume Databricks-produced Delta tables without duplication. This multi-platform architecture is common in large enterprises with both ML and SQL analytics requirements.

Cost comparison

Synapse Dedicated SQL Pool pricing is based on Data Warehouse Units (DWUs) — compute capacity billed hourly. 100 DWUs costs approximately $1.20/hour (Azure US East pricing at time of writing). Most production environments run 400–2,000 DWUs depending on query concurrency and data volume. A 1,000 DWU environment at full utilisation costs approximately $9,000/month in compute. Pause the pool when not in use to avoid charges. Storage (ADLS Gen2) is separate and inexpensive.

Synapse Serverless SQL is priced per TB of data processed ($5/TB in most regions). For ad-hoc exploration, this is economical; for high-volume analytical queries, costs can accumulate.

Databricks pricing on Azure is based on Databricks Units (DBUs) — the rate varies by cluster type. SQL compute (for analytics workloads) runs approximately $0.22/DBU on standard tier. A medium-sized SQL warehouse running 8 hours per day might consume 16 DBUs/hour — approximately $3,000/month. Interactive and jobs clusters have different DBU rates. The all-purpose compute (for notebooks and development) is more expensive per DBU than SQL and jobs compute.

At comparable workload scales, Databricks and Synapse are broadly similar in cost for SQL analytics. Databricks' advantage is pause-on-idle efficiency for variable workloads; Synapse Dedicated Pool's cost efficiency improves when consistently utilised.

Frequently asked questions

We are already on Synapse. Should we migrate to Databricks?

Unless your ML/AI requirements are significant and your current Synapse environment is not meeting them, migration is expensive and disruptive without clear ROI. If you are on Synapse Dedicated SQL Pool and primarily doing SQL analytics and Power BI reporting, stay there. If you are on Synapse and want to evaluate Microsoft's go-forward direction, look at Microsoft Fabric rather than Databricks.

Is Databricks replacing Azure Synapse?

For ML and AI workloads, Databricks has been the better platform for several years. For SQL analytics in Microsoft environments, Microsoft Fabric is Microsoft's answer to Databricks — not Synapse. Azure Synapse Analytics is still supported but is not receiving Microsoft's primary investment. New builds on Azure should evaluate Fabric over Synapse.

Can we use both?

Yes. Reading Databricks Delta tables via Synapse Serverless SQL or Fabric Direct Lake is a common pattern. Databricks produces engineered data assets in Delta Lake format; Synapse or Fabric consumes them for SQL analytics and Power BI reporting. This architecture avoids choosing between the platforms and lets each do what it does best.

For guidance on Azure data platform architecture — whether you are choosing between Synapse, Databricks, and Fabric, or designing the data layer that sits above the platform choice — our cloud engineering and data architecture consulting practices advise on Azure-specific patterns daily. Book a free 30-minute audit for a direct assessment of your specific situation.

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