Microsoft Fabric, Azure Databricks or Azure Synapse?
Three Azure analytics platforms with different centers of gravity. Microsoft Fabric is the SaaS platform on OneLake with Power BI built in, Azure Databricks is the Spark lakehouse with Unity Catalog and MLflow, and Azure Synapse Analytics is the earlier service from which Microsoft steers new investment toward Fabric.1
How they relate
Comparable. They can be alternatives for some goals; which one fits depends on the goal.
All three run Apache Spark and cover data engineering, warehousing and machine learning, so they overlap more than most Microsoft pairs. Microsoft Fabric is delivered as SaaS: its workloads, including Data Factory, Data Engineering, Data Warehouse, Real-Time Intelligence, Data Science and Power BI, share OneLake, a tenant-wide logical lake built on Azure Data Lake Storage, and you do not need an Azure account to use it. Azure Databricks is the Databricks Data + AI Platform on Azure, a lakehouse built on Spark with Delta Lake, MLflow and Unity Catalog. Azure Synapse Analytics combines dedicated and serverless SQL pools, Spark, pipelines and Data Explorer (preview) in Synapse Studio.
Microsoft's architecture guidance calls Fabric the recommended platform for new analytics-integrated machine learning investments and, for teams with an existing Synapse investment, says to prefer Fabric for new work. It points to Azure Databricks for Spark-scale machine learning, production MLOps and Unity Catalog governance, and notes that Fabric is not a replacement for a dedicated machine learning platform. Synapse keeps a few technical edges: Microsoft's Spark comparison lists GPU-accelerated pools, an external Hive Metastore and JDBC connections as reasons to use Synapse Spark, and Fabric does not support .NET for Spark.
They are not mutually exclusive. Fabric mirroring continuously replicates Azure Databricks and Snowflake data into OneLake, and OneLake shortcuts read data in place from Azure Data Lake Storage, Amazon S3 and Google Cloud Storage. You may also need none of them: Microsoft says Databricks cluster overhead might not be justified without Spark-scale processing, and in our view a single operational database feeding Power BI covers many modest reporting needs.12345
The technologies
Cloud application
Microsoft Fabric
Microsoft Fabric is Microsoft's SaaS analytics platform. Data integration, Spark data engineering, warehousing, real-time analytics, data science, operational databases and Power BI share one lake (OneLake) and one capacity-based license, so teams do not have to wire separate Azure services together.
Cloud service
Azure Databricks
Azure Databricks runs the Databricks Data + AI Platform on Azure: an Apache Spark lakehouse with Delta Lake tables, Unity Catalog governance, SQL warehouses, MLflow and model serving. It suits teams that need Spark-scale data engineering, machine learning and AI agents over their own cloud storage.
Cloud service
Azure Synapse Analytics
Azure Synapse Analytics is Azure's earlier analytics service, combining dedicated and serverless SQL pools, Apache Spark pools and Data Factory-style pipelines in one workspace. It is still supported, but Microsoft's guidance is to prefer Microsoft Fabric for new investments, and it provides migration tools.
Which fits which goal
| If your goal is | What fits | Why |
|---|---|---|
| Start a new analytics platform shared by engineers, data scientists and BI analysts | Microsoft Fabric | Microsoft positions Fabric as a fully managed SaaS platform where data engineering, data science and Power BI work on the same OneLake foundation, and recommends it for new analytics-integrated machine learning investments.1 |
| Train and serve machine learning models on very large datasets with Spark | Azure Databricks | Microsoft's guidance points to Azure Databricks for Spark-scale machine learning, managed model serving endpoints and MLOps pipelines.1 |
| Govern data, features and models in one catalog with lineage | Azure Databricks | Unity Catalog governs data, features, models and functions with centralized access control and lineage, which is why Microsoft rates Databricks primary for governance.1 |
| Keep running an existing warehouse on dedicated SQL pools | Azure Synapse Analytics | Microsoft still documents Synapse for existing investments but says to prefer Fabric for new ones, and offers a Fabric Migration Assistant for dedicated SQL pools when you are ready to move.1 |
| Run Spark jobs that need GPU pools, an external Hive Metastore or .NET for Spark | Azure Synapse Analytics | Microsoft's Fabric and Synapse Spark comparison lists these as reasons to choose Synapse Spark, because Fabric does not support them.5 |
| Report in Power BI on data that already lives in Databricks | Microsoft Fabric | Fabric mirroring continuously replicates Azure Databricks data into OneLake, where Power BI and other Fabric workloads can use it without a separate pipeline.2 |
No technology here is ranked. Pricing and performance are left out on purpose: they depend on your models, hardware and contract, so measure them in a pilot.
Sources
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