Azure Synapse Analytics vs Databricks: 22 Differences Explained

Choosing between Databricks and Azure Synapse Analytics can feel like picking between two powerful data analytics tools, each with its own strengths and unique features. Azure Synapse Analytics and Azure Databricks - both are powerful but designed for different journeys.

Azure Synapse is like an all-in-one business intelligence (BI) hub, perfect for structured data and reporting.

Databricks, on the other hand, is a data science powerhouse, built for complex analytics and AI-driven workloads.

Image source

If you need a fast, scalable data warehouse with built-in analytics, Azure Synapse is your go-to. But if you’re a data scientist handling massive, unstructured data, Databricks will serve you better.

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In this blog, we will discuss 23 key differences between Azure Databricks and Azure Synapse analytics to simplify your decision-making.

Azure Synapse Analytics vs Databricks: What’s the Difference?

Understanding the essential features of any platform is crucial prior to making comparisons. The following table shows feature differences between Azure Synapse and Databricks.

Image source

Let’s break down their core differences in the simplest way possible.

1. Azure Synapse Analytics vs Azure Databricks: Data Integration

• Synapse makes data integration easy with Synapse Pipelines, working like a built-in Azure Data Factory.
• Databricks, however, requires a separate integration with Azure Data Factory to handle data workflows.

2. Databricks vs Azure Synapse Analytics: Data Storage

• Synapse connects seamlessly with SQL Data Warehouse, Azure Data Lake, and Cosmos DB.
• Databricks prefers Delta Lake for optimized storage but also integrates with S3 and ADLS for flexibility.

Read More: 17 Reasons Why Companies Migrate from AWS to Azure

3. Synapse vs Databricks: Compute Engine

• Imagine Synapse as a SQL-first machine, optimized for structured data. It offers serverless and dedicated computing options.
• Databricks, however, is built on Apache Spark,excelling at massive-scale data processing.

4. Azure Databricks vs Synapse Analytics: Scalability variations

• Synapse scales efficiently with on-demand and provisioned computing.
• Databricks takes it further with auto-scaling clusters, adjusting resources dynamically based on workload.

Meanwhile, take a look at the expert opinions on Synapse and Databricks here

5. Azure Synapse Analytics vs Azure Databricks: Data Processing

• Need fast batch and real-time data processing? Synapse provides both via its SQL pools.
• But for truly complex, big data workflows, Databricks’ Apache Spark engine is hard to beat.

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6. Azure Synapse Analytics vs Databricks: Query Performance

• Synapse is optimized for fast structured data queries with its distributed query engine
• Databricks handles both structured and unstructured data efficiently using Spark’s parallel processing.

Image source

7. Azure Synapse Analytics vs Databricks: AI/ML Capabilities

• Synapse offers Azure Machine Learning integration with T-SQL support.
• Databricks has MLflow and Spark MLlib, making it the better choice for serious AI/ML projects.

8. Azure Synapse Analytics vs Databricks: Notebooks

• Both platforms support notebooks, but Synapse integrates them into Synapse Studio with Python, Spark SQL, Scala, and R
• Databricks offers a more advanced notebook environment, preferred by data scientists.

Read More: Azure DevOps Pipeline Deployment for Competitive Business: The Winning Formula

9. Synapse vs Databricks Cost Model

• Synapse follows a pay-as-you-go model, charging separately for storage and computing. It offers a Pre-purchase units option.
• Databricks has a similar pricing structure but is optimized for big data, making it cost-efficient for large-scale processing. It offers a 14-day free trial.

10. Azure Synapse Analytics vs Databricks: Security

Both platforms offer data encryption and role-based access control, but Synapse integrates deeply with Azure Private Endpoints for additional security layers.

11. Azure Synapse Analytics vs Databricks: Collaboration

• If your team relies on Azure DevOps and GitHub, Synapse is a natural fit.
• Databricks goes beyond Databricks Repos, providing richer collaboration tools for teams working on big data and AI.

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12. Azure Synapse Analytics vs Databricks: Developer Experience

• Synapse feels like drag-and-drop analytics, making it easier for business users.
• Databricks offers a data scientist-friendly workspace, loaded with notebooks and AI-driven insights.

13. Synapse Analytics vs Databricks: Interoperability

• Synapse deeply integrates with Power BI, Azure ML, and Logic Apps.
• Databricks also works well with Azure but offers multi-cloud support, making it more flexible.

Read More: Is It Worth Using Azure With Power Platforms For Financial Business?

14. Azure Synapse Analytics vs Databricks: Data Governance

• Synapse has built-in Azure Purview integration, making data governance easier.
• With Databricks, you’ll need to configure external governance tools for compliance tracking.

15. Azure Synapse Analytics vs Databricks: Support for BI Tools

• If Power BI is your primary BI tool, Synapse is the better choice.
• Databricks, however, integrates with Qlik, Looker, Power BI or Tableau, making it a great fit for diverse analytics environments.

Image source

16. Azure Synapse Analytics vs Databricks: Ease of Use

• Need a user-friendly platform? Synapse offers an intuitive, business-user-friendly interface.
• Databricks requires technical expertise, making it ideal for data engineers and scientists.

17. Azure Synapse Analytics vs Databricks: Multi-Cloud Support

• Synapse is Azure-centric, offering a consumption-based pricing model.
• Databricks, however, works across Azure, AWS, and GCP, giving you the freedom to choose your cloud provider.

18. Azure Synapse Analytics vs Databricks: Real-Time Analytics

• Synapse supports real-time analytics with Azure Stream Analytics.
• Databricks, using Spark Streaming, offers powerful real-time processing for big data.

19. Azure Synapse Analytics vs Databricks: AI Integration

• Synapse works best with Azure Cognitive Services and Azure ML.
• Databricks provides deep learning and AI model training, making it a stronger choice for AI-heavy projects.

See what the experts on Reddit says about choosing between Databricks and Synapse Analytics for a small data science team.

• The discussion highlights the need for building a complete data stack for machine learning, data engineering, and analytics.
• Experts also emphasized the importance of deciding which tool to use and provided reasons for their recommendation.

Looking to transit from AWS to Azure? Partner with us now.

Get started

20. Azure Synapse Analytics vs Databricks: Compliance and Certifications

Both platforms meet GDPR, HIPAA, and other compliance standards, ensuring enterprise-grade security and governance.

Azure Synapse:

• Ideal for data warehousing and integration.
• Native integration with other Azure services.

Databricks:

• Focused on data science and machine learning.
• Suitable for complex data pipelines.

Read More: Azure SQL vs MySQL Database: Which is Right for You?

21. Azure Synapse Analytics vs Databricks: Deployment & Monitoring

• Synapse is fully managed, with automatic updates handled by Microsoft.
• Databricks, while managed, give you more control over cluster configurations and auto-scaling.

22. Azure Synapse Analytics vs Databricks: Community and Ecosystem

• Synapse benefits from Microsoft’s strong enterprise support.
• Databricks, built on open-source Apache Spark, has a vast community and extensive plugin support.

Azure Synapse Analytics vs Databricks: 22 Differences Explained Choosing between Databricks and Azure Synapse Analytics can feel like picking between two powerful data analytics tools, each with its own strengths and unique features. Azure Synapse Analytics and Azure Databricks - both are powerful but designed for different journeys. Azure Synapse is like an all-in-one business intelligence (BI) hub, perfect for structured data and reporting. Databricks, on the other hand, is a data science powerhouse, built for complex analytics and AI-driven workloads. Image source If you need a fast, scalable data warehouse with built-in analytics, Azure Synapse is your go-to. But if you’re a data scientist handling massive, unstructured data, Databricks will serve you better. Securely manage identities and access with Microsoft Entra ID Services. Get in touch In this blog, we will discuss 23 key differences between Azure Databricks and Azure Synapse analytics to simplify your decision-making. Azure Synapse Analytics vs Databricks: What’s the Difference? Understanding the essential features of any platform is crucial prior to making comparisons. The following table shows feature differences between Azure Synapse and Databricks. Image source Let’s break down their core differences in the simplest way possible. 1. Azure Synapse Analytics vs Azure Databricks: Data Integration Synapse makes data integration easy with Synapse Pipelines, working like a built-in Azure Data Factory. Databricks, however, requires a separate integration with Azure Data Factory to handle data workflows. 2. Databricks vs Azure Synapse Analytics: Data Storage Synapse connects seamlessly with SQL Data Warehouse, Azure Data Lake, and Cosmos DB. Databricks prefers Delta Lake for optimized storage but also integrates with S3 and ADLS for flexibility. Read More: 17 Reasons Why Companies Migrate from AWS to Azure 3. Synapse vs Databricks: Compute Engine Imagine Synapse as a SQL-first machine, optimized for structured data. It offers serverless and dedicated computing options. Databricks, however, is built on Apache Spark,excelling at massive-scale data processing. 4. Azure Databricks vs Synapse Analytics: Scalability variations Synapse scales efficiently with on-demand and provisioned computing. Databricks takes it further with auto-scaling clusters, adjusting resources dynamically based on workload. Meanwhile, take a look at the expert opinions on Synapse and Databricks here 5. Azure Synapse Analytics vs Azure Databricks: Data Processing Need fast batch and real-time data processing? Synapse provides both via its SQL pools. But for truly complex, big data workflows, Databricks’ Apache Spark engine is hard to beat. Optimize your business operations with our comprehensive Microsoft Azure Cloud Solutions. Let’s Discuss Now 6. Azure Synapse Analytics vs Databricks: Query Performance Synapse is optimized for fast structured data queries with its distributed query engine Databricks handles both structured and unstructured data efficiently using Spark’s parallel processing. Image source 7. Azure Synapse Analytics vs Databricks: AI/ML Capabilities Synapse offers Azure Machine Learning integration with T-SQL support. Databricks has MLflow and Spark MLlib, making it the better choice for serious AI/ML projects. 8. Azure Synapse Analytics vs Databricks: Notebooks Both platforms support notebooks, but Synapse integrates them into Synapse Studio with Python, Spark SQL, Scala, and R Databricks offers a more advanced notebook environment, preferred by data scientists. Read More: Azure DevOps Pipeline Deployment for Competitive Business: The Winning Formula 9. Synapse vs Databricks Cost Model Synapse follows a pay-as-you-go model, charging separately for storage and computing. It offers a Pre-purchase units option. Databricks has a similar pricing structure but is optimized for big data, making it cost-efficient for large-scale processing. It offers a 14-day free trial. 10. Azure Synapse Analytics vs Databricks: Security Both platforms offer data encryption and role-based access control, but Synapse integrates deeply with Azure Private Endpoints for additional security layers. 11. Azure Synapse Analytics vs Databricks: Collaboration If your team relies on Azure DevOps and GitHub, Synapse is a natural fit. Databricks goes beyond Databricks Repos, providing richer collaboration tools for teams working on big data and AI. Deploy and scale your applications effortlessly with Azure App Services. Contact Now 12. Azure Synapse Analytics vs Databricks: Developer Experience Synapse feels like drag-and-drop analytics, making it easier for business users. Databricks offers a data scientist-friendly workspace, loaded with notebooks and AI-driven insights. 13. Synapse Analytics vs Databricks: Interoperability Synapse deeply integrates with Power BI, Azure ML, and Logic Apps. Databricks also works well with Azure but offers multi-cloud support, making it more flexible. Read More: Is It Worth Using Azure With Power Platforms For Financial Business? 14. Azure Synapse Analytics vs Databricks: Data Governance Synapse has built-in Azure Purview integration, making data governance easier. With Databricks, you’ll need to configure external governance tools for compliance tracking. 15. Azure Synapse Analytics vs Databricks: Support for BI Tools If Power BI is your primary BI tool, Synapse is the better choice. Databricks, however, integrates with Qlik, Looker, Power BI or Tableau, making it a great fit for diverse analytics environments. Image source 16. Azure Synapse Analytics vs Databricks: Ease of Use Need a user-friendly platform? Synapse offers an intuitive, business-user-friendly interface. Databricks requires technical expertise, making it ideal for data engineers and scientists. 17. Azure Synapse Analytics vs Databricks: Multi-Cloud Support Synapse is Azure-centric, offering a consumption-based pricing model. Databricks, however, works across Azure, AWS, and GCP, giving you the freedom to choose your cloud provider. 18. Azure Synapse Analytics vs Databricks: Real-Time Analytics Synapse supports real-time analytics with Azure Stream Analytics. Databricks, using Spark Streaming, offers powerful real-time processing for big data. 19. Azure Synapse Analytics vs Databricks: AI Integration Synapse works best with Azure Cognitive Services and Azure ML. Databricks provides deep learning and AI model training, making it a stronger choice for AI-heavy projects. See what the experts on Reddit says about choosing between Databricks and Synapse Analytics for a small data science team. The discussion highlights the need for building a complete data stack for machine learning, data engineering, and analytics. Experts also emphasized the importance of deciding which tool to use and provided reasons for their recommendation. Looking to transit from AWS to Azure? Partner with us now. Get started 20. Azure Synapse Analytics vs Databricks: Compliance and Certifications Both platforms meet GDPR, HIPAA, and other compliance standards, ensuring enterprise-grade security and governance. Azure Synapse: Ideal for data warehousing and integration. Native integration with other Azure services. Databricks: Focused on data science and machine learning. Suitable for complex data pipelines. Read More: Azure SQL vs MySQL Database: Which is Right for You? 21. Azure Synapse Analytics vs Databricks: Deployment & Monitoring Synapse is fully managed, with automatic updates handled by Microsoft. Databricks, while managed, give you more control over cluster configurations and auto-scaling. 22. Azure Synapse Analytics vs Databricks: Community and Ecosystem Synapse benefits from Microsoft’s strong enterprise support. Databricks, built on open-source Apache Spark, has a vast community and extensive plugin support. Final Thoughts: Which One Should You Choose? With all the key clarifications provided, the next question that surfaces is, "Is Synapse preferable to Databricks, or is it the opposite?" The response is influenced by a range of considerations. With all the key clarifications provided, the next question that surfaces is - “Is Synapse better than Databricks? or is it the other way around?”. To put it simply, it depends! Pick Azure Synapse if you need a user-friendly, SQL-driven analytics platform deeply integrated with Power BI. Choose Databricks if you’re handling massive datasets, AI models, and real-time big data processing with Apache Spark. So, that’s all about Azure Databricks vs Azure Synapse Analytics. We hope you found this helpful. If you are looking to migrate your AWS workflows to Azure or you want to learn more about our Azure migration services, do not hesitate to contact us. We are just a click away! FAQs on Databricks vs Azure Synapse Analytics 1. What is the difference between Databricks and Azure Synapse Analytics? Databricks: An Apache Spark-based analytics platform optimized for Azure, designed for big data processing, machine learning, and real-time analytics. Azure Synapse Analytics: A unified analytics service that combines data integration, enterprise data warehousing, and big data analytics. See what experts believe about this. Click here. 2. What is the difference between Azure and Azure Synapse? Azure: A comprehensive cloud platform offering a wide range of services including computing, storage, networking, and more. Azure Synapse: A specific service within Azure focused on analytics, integrating data warehousing, big data, and data integration capabilities. Check out what the certified Microsoft experts say about it. Click here. 3. What is the difference between Azure and Azure Databricks? Azure: The overarching cloud platform providing various services across different domains. Azure Synapse: A collaborative, Apache Spark-based analytics service within Azure, tailored for data engineering, data science, and machine learning Learn more about Azure vs Azure Databricks here. 4. Difference between Azure Synapse Analytics and Azure Stream Analytics Azure Synapse Analytics: Focuses on data warehousing and big data analytics, providing a unified experience for data integration and analysis. Azure Stream Analytics: A real-time analytics service designed for processing and analyzing streaming data from various sources. Learn more about Azure Synapse vs Azure Stream Analytics here. 5. When to use Synapse Spark pool vs Azure Databricks Synapse Spark Pool: Best for users already within the Synapse ecosystem, needing integrated data warehousing and analytics with features like autoscale and dynamic allocation. Azure Databricks: Ideal for collaborative data engineering and data science projects, offering advanced machine learning capabilities and integration with Azure services.

Vinod Satapara

A technocrat and entrepreneur of iFour Technolab Pvt. Ltd. with extensive experience in building enterprise web, cloud, and mobile applications using ASP.NET, CORE, .NET MVC, Angular, and Blockchain. Passionate about addressing business problems through innovative technologies, Vinod is also an avid reader, enjoys exploring new tech trends, and loves traveling in his spare time.