As a data engineer navigating cloud platforms and analytics ecosystems, I’ve worked extensively with both Databricks and Azure Synapse Analytics. While they often appear side-by-side in Azure environments, they come from different design philosophies and cater to slightly different needs—even when they seem to do many of the same things.
Both platforms allow you to spin up compute clusters, run Python notebooks, and integrate with orchestration tools like Azure Data Factory. But dig a little deeper, and the differences become significant enough to impact cost, development experience, and time to deployment.
In this post, I’ll break down the practical differences and overlaps between these two platforms—highlighting lessons from the field and offering strategic insight to help you make informed technology decisions.
Two Analytics Powerhouses in the Azure Ecosystem
Let’s start by clarifying what each tool is.
Azure Synapse Analytics is Microsoft’s integrated analytics platform that combines big data and data warehousing capabilities. It allows users to query data using both T-SQL (via dedicated SQL pools) and Apache Spark (via Spark pools). Synapse aims to unify structured and unstructured analytics under one roof, integrated tightly with Azure Data Lake, Power BI, and Azure Active Directory.
Databricks, originally built around Apache Spark, is a unified analytics platform developed by the creators of Spark. It’s available on Azure (as Azure Databricks) but also on AWS and GCP. It offers a highly interactive notebook environment supporting Python, Scala, SQL, and R, with strong support for machine learning, data science workflows, and real-time streaming.
Both are part of the same generation of cloud-native tools for modern data engineering, but they take different approaches in architecture, pricing, and optimization.
To access Azure Synapse Analytics, start by navigating to the Azure Portal and searching for “Synapse Analytics” in the top search bar. Click “Create” to provision a new Synapse workspace, specifying your resource group, workspace name, storage account, and file system. Once created, you can launch the Synapse Studio directly from the workspace overview, where you can manage data, develop notebooks, and monitor pipelines.
For Azure Databricks, search for “Azure Databricks” in the portal, then click “Create” to configure a workspace with your desired region and pricing tier. After deployment, open the Databricks workspace from the resource blade, which redirects you to the Databricks UI, where you can create clusters, import notebooks, and manage libraries. In both cases, you’ll use Azure Active Directory to assign access roles and Azure Data Factory if you need to orchestrate jobs across platforms.
Key Similarities
Despite different origins, both platforms offer a similar baseline of features:
- Notebook-style development: Both support Python notebooks and Spark processing.
- Elastic compute: You can scale up or down clusters on demand.
- Integration with Azure Data Factory: Both can be scheduled and orchestrated as activities in ADF pipelines.
- Security & Identity: Both leverage Azure Active Directory for role-based access control.
For many use cases, either platform will technically work. But not all platforms are created equal in cost, usability, or flexibility.
My Experience: Portability Isn’t Seamless
In one project, I had the opportunity to run the same notebook on both platforms—once in Azure Databricks, and once in Azure Synapse. While both completed the task, Databricks incurred noticeably higher costs for the same job.
When I tried migrating notebooks from Databricks to Synapse, I learned firsthand that Python library support differed. Certain packages that were pre-installed in Databricks (like MLflow or DBUtils) were absent or harder to configure in Synapse. It took extra effort to adjust the codebase, configure packages, and re-test.
Also, configuring permissions in Synapse was more intricate. I had to collaborate with our cloud engineer to iron out permissions on storage accounts and workspaces. These differences slowed us down—despite Synapse’s lower compute cost.
Where Databricks Shines
- Advanced Data Science & ML: Databricks provides built-in ML lifecycle tools like MLflow, automatic logging, experiment tracking, and native support for distributed training.
- Collaborative Development: Its notebook UI is polished, with real-time co-authoring, version control, and integrated comments.
- Cross-cloud Availability: While Synapse is exclusive to Azure, Databricks runs on AWS, GCP, and Azure, making it ideal for hybrid or multi-cloud strategies.
- Streaming & Delta Lake: Native support for Delta Lake, structured streaming, and complex data engineering pipelines is more mature.
Where Synapse Excels
- Cost-Effective SQL & Data Lake Analytics: Synapse is ideal when your workloads are mostly SQL-based, or when you’re blending T-SQL queries over files stored in Azure Data Lake.
- Integrated with Azure Stack: From a management and security standpoint, Synapse feels more like a native part of Azure. It benefits from tighter integration with Azure Monitor, Purview, and Power BI.
- Single Pane for BI & DW: If your organization has a strong BI presence and wants to mix data warehouse and data lake analytics in one place, Synapse is a natural fit.
Decision Guidance
Here’s a rough rule of thumb based on my experience and industry patterns sourced through AI:
| Scenario | Best Fit |
|---|---|
| SQL-focused workloads and data warehousing | Azure Synapse |
| Advanced machine learning, real-time streaming | Databricks |
| Multi-cloud deployments | Databricks |
| Lower-cost Python-based batch jobs | Azure Synapse (with caveats) |
| Teams with deep Spark experience | Databricks |
| Deep Azure-native stack (Purview, Power BI, Defender) | Azure Synapse |
Databricks and Azure Synapse Analytics are not adversaries—they can coexist in the same enterprise data strategy. In fact, in our case, we used both, choosing the right tool for the job and coordinating execution through Azure Data Factory.
But they aren’t interchangeable. Choosing between them depends on your data team’s skillset, your cost constraints, and the nature of your analytics workloads.
If you are evaluating the next evolution of your data platform, don’t just ask what each tool can do. Ask how it fits into your long-term architecture and which team will be maintaining it. Sometimes, the best solution is hybrid—just make sure your team is ready to manage the complexity.
