Choosing the right platform to work with your data can be confusing and especially with so many tools available. Two popular options that often come up are Azure Synapse and Databricks. Both are powerful and cloud-based platforms that help businesses store, process, and analyze large amounts of data.
But they are designed for slightly different purposes. While Azure Synapse is great for data warehousing and business intelligence tasks using SQL, Databricks is more focused on big data, machine learning, and real-time analytics using Spark. In this blog we will break down the differences between Azure Synapse and Databricks in the simplest way possible.
Whether you are a beginner, a working professional and overall involved with the tech team. In this guide we will help you decide which tool is better suited for your needs. Keep reading to learn about features, pricing, use cases, and more in this Azure Synapse vs Databricks comparison.
What is Azure Synapse?
Azure Synapse is a cloud-based platform from Microsoft that combines big data and data warehousing in one place. It lets you analyze large volumes of data using either SQL queries or Apache Spark. With Apache Synapse, you can pull data easily from multiple sources that include files, databases, or Azure Data Lake and then run reports, build dashboards or create machine learning models.
One of the best things about Azure Synapse is that it works smoothly with other Microsoft tools like Power BI, Azure Data Factory and Azure Machine Learning. If your team mostly uses SQL and relies on Microsoft’s ecosystem then Apache Synapse makes it easy to manage and analyze data without switching platforms.
What is Databricks?
In the debate of Azure Synapse vs Databricks, Databricks stands out as a cloud platform made for big data and machine learning. It runs on Apache Spark and supports large-scale data processing. Databricks offers a shared workspace where teams can write code in Python, SQL, Scala, or R. It includes tools like notebooks for writing and testing code, and MLflow for managing machine learning projects.
The platform follows a lakehouse model using Delta Lake. This combines the flexibility of data lakes with the reliability of data warehouses. It allows users to work with both raw and structured data in one place.
Databricks is a strong choice if your focus is on building data pipelines, running AI models, or handling real-time analytics. When comparing Azure Synapse vs Databricks, choose Databricks for advanced analytics and fast, scalable data workflows.
Apache Synapse vs Databricks: 10 Key Differences
In this section we will see the top 10 key differences between Azure Synapse and Databricks. Have a look at the table below comprise of 10 amazing between these two tools i.e, Azure Synapse vs Databricks:
| Aspect | Azure Synapse Analytics | Databricks |
| Primary Use Case | Designed for data warehousing business intelligence and structured reporting | Geared towards big data processing machine learning and advanced analytics |
| Underlying Engine | Utilizes TSQL as its core query language with optional integration of Apache Spark | Built entirely on Apache Spark offering high performance and scalability |
| Programming Languages | Supports SQL and limited PySpark functionality | Provides native support for Python SQL Scala and R |
| User Experience | Offers Synapse Studio with separate environments for SQL and Spark | Provides collaborative notebooks with unified development environments |
| Data Storage Approach | Integrates with Azure Data Lake and supports dedicated and serverless SQL pools | Uses Delta Lake to enable lakehouse architecture for unified data management |
| Machine Learning | Integrates externally with Azure Machine Learning services | Includes built-in MLflow for managing machine learning workflows |
| Streaming Capabilities | Integrates with Azure Stream Analytics for real time processing | Offers native support for structured streaming through Apache Spark |
| Performance Management | Includes basic optimization with limited tuning capabilities | Enables fine-grained control and performance tuning using Spark configurations |
| Pricing Structure | Offers serverless pay per query and provisioned models based on compute and storage usage | Charges based on Databricks Units which vary by cluster size and runtime |
| Platform Integration | Best suited for enterprises using Microsoft Azure products and services | Suitable for teams requiring flexible analytics AI development and scalability |
When to Use Azure Synapse?
There are 5 main points that i am mentioning today to ensure the usage of Azure Synapse.
For Enterprise-Level Reporting and BI
Use Azure Synapse when your organization relies heavily on dashboards and structured reports using tools like Power BI.
When Working with Structured Data
Ideal for querying structured data from relational databases using SQL for analytics and insights.
Need for Seamless Integration with Microsoft Tools
Choose Synapse if your tech stack already includes services like Azure Data Lake, Azure Machine Learning, and Power BI.
Data Warehousing at Scale
Use Synapse for building scalable cloud data warehouses that support complex queries and large datasets.
Cost-Efficient Serverless Querying
Synapse is effective when you need to run ad hoc queries without setting up or managing infrastructure.
When to Use Databricks?
For Big Data Processing and ETL Pipelines
Databricks is ideal when you need to process large volumes of raw or semi-structured data with speed and efficiency.
When Working with Machine Learning and AI Projects
Use Databricks for end-to-end machine learning workflows, from data preparation to model deployment, using tools like MLflow.
Real-Time Data Streaming and Analytics
It is well-suited for real-time data applications using structured streaming with low-latency processing.
Collaboration Across Data Teams
Databricks offers shared notebooks that allow data engineers, analysts, and scientists to collaborate in one unified environment.
Lakehouse Architecture Adoption
Choose Databricks when implementing a lakehouse model, combining the flexibility of data lakes with the structure of data warehouses using Delta Lake.
Conclusion
In the comparison of Azure Synapse vs Databricks, the right choice depends on your goals. If you are focused on SQL based analytics, reporting, and deep integration with Microsoft tools, Azure Synapse is the way to go. If your team needs to process large data sets, build AI models, or run real-time analytics, then Databricks is the better option.
By understanding the strengths of both platforms, you can build a modern data strategy that balances performance, scalability, and cost.
Frequently Asked Questions (Azure Synapse vs Databricks)
What is the main difference between Azure Synapse and Databricks?
Azure Synapse is mainly used for data warehousing and business intelligence It is ideal for structured data and SQL-based analytics Databricks on the other hand is designed for big data processing machine learning and advanced analytics using Apache Spark.
Can Azure Synapse and Databricks be used together?
Yes both platforms can be integrated You can use Databricks for data engineering and machine learning tasks and then push the cleaned data into Synapse for business reporting and dashboards using Power BI.
Which is better for machine learning tasks?
Databricks is better suited for machine learning It provides built-in support for MLflow collaborative notebooks and large-scale model training Synapse supports ML through integration with Azure Machine Learning but it is not designed as a native ML platform.