In this comparison, I’ll share the difference between Azure Synapse Analytics and Databricks. We’ll explore architecture differences, performance considerations, pricing models, and real-world implementation.
Table of Contents
Azure Synapse vs Databricks
Before diving into detailed comparisons, it’s essential to understand that Azure Synapse Analytics and Azure Databricks serve somewhat different purposes within Microsoft’s data platform, despite significant overlap in capabilities.
Azure Synapse Analytics is Microsoft’s integrated analytics service that brings together enterprise data warehousing and big data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources, at scale.
Check out Azure Synapse Analytics Tutorials for more details.
Azure Databricks, on the other hand, is a fast, easy, and collaborative Apache Spark-based analytics service designed for data science and data engineering. It was built in collaboration between Microsoft and Databricks to bring the powerful open-source analytics platform to Azure.
Let’s examine their key architectural differences:
| Feature | Azure Synapse Analytics | Azure Databricks |
|---|---|---|
| Core Engine | SQL (serverless & dedicated) + Spark | Apache Spark |
| Primary Focus | Data integration & enterprise analytics | Data engineering & data science |
| Governance Model | Integrated with Azure Purview | Databricks Unity Catalog |
| Development Experience | Synapse Studio (integrated) | Notebooks & Jobs |
| Deployment Model | Azure-native service | Azure-hosted partner service |
| Data Lake Integration | Built-in with Azure Data Lake | Requires configuration |
| Machine Learning | Azure ML integration | MLflow native integration |
Having deployed both platforms across various scenarios, I’ve found that understanding these fundamental differences is crucial for making the right architectural decisions.
Key Decision Criteria: When to Choose Each Platform
Choose Azure Synapse Analytics When:
- You Need Enterprise Data Warehousing at Scale.
- You Want Integrated Data Integration and Analytics.
- Seamless Integration with Power BI is Critical.
- SQL-First Experience is Preferred. For organizations with strong SQL skills, Synapse offers both serverless and dedicated SQL experiences that leverage existing team expertise.
Choose Azure Databricks When:
- Advanced Data Science and ML Workflows are Primary.
- Open-Source Ecosystem Alignment is Important.
- Performance for Complex ETL Processing is Critical.
- Data Science Collaboration is a Priority.
Performance Comparison
Data Warehousing Workloads
For traditional data warehousing with structured data and SQL queries:
- Synapse Dedicated SQL Pools: Excels at complex joins, aggregations, and high concurrency scenarios. For a retail client processing 5+ billion rows, we achieved consistent sub-second query performance.
- Databricks SQL: Improving rapidly, but still generally slower for complex SQL queries with multiple joins compared to dedicated SQL pools.
Big Data Processing
For large-scale data transformation and ETL:
- Synapse Spark Pools: Adequate for most batch processing needs, but generally not as optimized as Databricks.
- Databricks with Photon: Significantly faster for large-scale transformations. In one implementation, we saw 40% faster processing times compared to Synapse Spark for the same workloads.
Machine Learning Workloads
- Synapse: Requires more integration work with Azure ML for end-to-end ML pipelines.
- Databricks: Native MLflow integration and optimized ML libraries provide a more streamlined experience.
Cost Considerations
Cost optimization is always a critical factor in platform selection.
Azure Synapse Analytics Cost Factors
- Dedicated SQL Pools: Priced per DWU (Data Warehouse Unit) per hour, whether queries are running or not.
- Serverless SQL: Pay per TB of data processed, ideal for intermittent workloads.
- Spark Pools: Charged per vCore-hour when clusters are running.
- Data Movement: Costs for data ingestion and integration activities.
Azure Databricks Cost Factors
- DBUs (Databricks Units): Pricing based on the Databricks runtime tier (Standard, Premium, Enterprise).
- Cluster Configuration: Costs scale with node size and count.
- Minimum Clusters: Often requires separate clusters for different workloads, resulting in increased costs.
- Autoscaling: Can help optimize costs, but requires careful configuration.
Cost Optimization Strategies
- For Synapse:
- Use pause/resume scheduling for dedicated SQL pools during off-hours
- Leverage serverless SQL for ad-hoc analytics
- Right-size dedicated pools based on actual query performance needs
- For Databricks:
- Implement aggressive autoscaling
- Use cluster scheduling and job clusters
- Consider reserved instances for persistent workloads
Security and Governance Comparison
Here’s how the platforms compare:
Azure Synapse Analytics
- Network Security: Private endpoints, VNet integration, IP firewall
- Authentication: Azure AD integration with fine-grained access control
- Data Protection: Column-level security, dynamic data masking, row-level security
- Auditing: Comprehensive query and access auditing
- Governance: Integration with Azure Purview for data discovery and lineage
Azure Databricks
- Workspace Security: Secure cluster connectivity, private link support
- Authentication: Azure AD integration, but with a separate permission model
- Data Protection: Table Access Control (in Unity Catalog)
- Auditing: Audit logs for workspace activity
- Governance: Unity Catalog for cross-workspace governance (newer capability)
Integration Capabilities
Azure Synapse Analytics Integrations
- Azure Platform: Native integration with Azure Data Lake, Azure ML, Power BI, Azure Purview
- Data Sources: 95+ connectors for various data sources
- Development Tools: Synapse Studio, Azure DevOps, GitHub integration
- Orchestration: Built-in pipeline capabilities
Azure Databricks Integrations
- Azure Ecosystem: Integration with Azure Data Lake, Azure ML (requires configuration)
- Data Sources: Supports multiple data sources via Spark connectors
- Development Tools: Notebooks, Jobs, Git integration
- Orchestration: Workflows for orchestration, but many clients still use ADF
Real-World Implementation
In my consulting practice, I’ve found that many enterprises actually benefit from using both platforms together in a complementary architecture. Here’s a pattern I’ve successfully implemented for several Fortune 500 clients:
- Use Databricks for:
- Advanced data engineering with complex transformations
- Data science and machine learning workloads
- Exploratory data analysis
- Processing unstructured and semi-structured data
- Use Synapse for:
- Enterprise data warehousing
- Business intelligence and reporting
- Data governance and security
- Structured data analytics at scale
- Integration Points:
- Databricks processes raw data and performs complex transformations
- Results are stored in the Data Lake in optimized formats
- Synapse serverless SQL provides views over processed data
- Power BI connects to Synapse for business user access
Let’s point out some key differences between Azure Synapse Analytics and Azure Databricks.
| Related Topics | Azure Synapse Analytics | Databricks |
| Supported Languages | It supports multiple programming languages like Python, SQL, Scala, Java, etc. | Databricks supports different programming languages like SQL, Python, R, etc. |
| Developer’s Point of View | Comes with Azure Synapse Studio, which makes the development more accessible, and it’s a single place for accessing multiple services. | Here, you will get the Databricks Connect and UI to work with. |
| Support for Notebooks | Notebooks are supported here with no automated versioning feature. The supported notebook is Nteract Notebooks. | Notebooks are supported here with an automated versioning feature, which helps a lot. The supported notebook is Databricks Notebooks. |
| Which type of Tool? | Basically, known as a data warehouse and analytics tool. | It is basically known as a notebook tool that is Spark-based. |
Supported Spark | Supports Apache Spark (Open-source). | Supports the latest version of Apache Spark and Spark 3.0. |
| Data Lake | At the time of the creation of the Azure Synapse, you need to choose the primary Data Lake. | You have to install the Data Lake separately. |
| T-SQL experience | You have the provision to enjoy a complete T-SQL experience | Here, you won’t get a complete T-SQL experience. |
| Power BI experience | Here, you can use Power BI for reporting from the Azure Synapse Studio. | Here, you will get the full SQL traditional BI. |
FAQs
Can Databricks connect to Synapse?
Yes, it is possible to connect Azure Synapse from Azure Databricks using the Azure Synapse connector.
Is Azure Synapse analytics PaaS or SAAS?
Azure Synapse Analytics is a Paas ( Platform as a Service ).
Conclusion
The choice between Azure Synapse Analytics and Azure Databricks ultimately depends on your specific use cases, existing skillsets, and strategic direction. Based on my experience implementing both platforms:
- Choose Synapse if enterprise data warehousing, integrated analytics, and tight Azure integration are your primary requirements.
- Choose Databricks if advanced data science, machine learning excellence, and Spark-based processing are your priorities.
- Consider a hybrid approach if you have diverse needs spanning both areas.
Remember that both platforms continue to evolve rapidly, with Synapse enhancing its Spark capabilities and Databricks strengthening its SQL and data warehousing features.
You may also like the following articles below
- Azure Databricks Tutorial
- How to Create Azure Synapse Analytics
- Azure Synapse Analytics vs Azure Data Factory
- Azure Synapse Analytics Tutorial
I am Rajkishore, and I am a Microsoft Certified IT Consultant. I have over 14 years of experience in Microsoft Azure and AWS, with good experience in Azure Functions, Storage, Virtual Machines, Logic Apps, PowerShell Commands, CLI Commands, Machine Learning, AI, Azure Cognitive Services, DevOps, etc. Not only that, I do have good real-time experience in designing and developing cloud-native data integrations on Azure or AWS, etc. I hope you will learn from these practical Azure tutorials. Read more.
