Today, data engineers have a wide array of tools and platforms at their disposal for data engineering projects. Popular choices include Microsoft Fabric, Azure Synapse Analytics (ASA), Azure Data Factory (ADF), and Azure Databricks (ADB). It’s common to wonder which one is the best fit for your specific needs.
Side by Side comparison
Here’s a concise comparison of Microsoft Fabric, Azure Synapse Analytics, Azure Data Factory (ADF), and Azure Databricks (ADB) based on their key features, use cases, and differences:
| Feature | Microsoft Fabric | Azure Synapse Analytics | Azure Data Factory (ADF) | Azure Databricks (ADB) |
|---|---|---|---|---|
| Type | Unified SaaS analytics platform | Integrated analytics service | Cloud ETL/ELT service | Apache Spark-based analytics platform |
| Primary Use Case | End-to-end analytics (Data Engineering, Warehousing, BI, Real-Time) | Large-scale data warehousing & analytics | Data integration & orchestration | Big Data processing, ML, AI, advanced analytics |
| Data Integration | Built-in Data Factory capabilities | Synapse Pipelines (similar to ADF) | Hybrid ETL/ELT pipelines | Limited (relies on Delta Lake, ADF, or custom code) |
| Data Warehousing | OneLake (Delta-Parquet based) | Dedicated SQL pools (MPP) | Not applicable | Can integrate with Synapse/Delta Lake |
| Big Data Processing | Spark-based (Fabric Spark) | Spark pools (serverless/dedicated) | No (orchestration only) | Optimized Spark clusters (Delta Lake) |
| Real-Time Analytics | Yes (Real-Time Hub) | Yes (Synapse Real-Time Analytics) | No | Yes (Structured Streaming) |
| Business Intelligence | Power BI (deeply integrated) | Power BI integration | No | Limited (via dashboards or Power BI) |
| Machine Learning | Basic ML integration | ML in Spark pools | No | Full ML/DL support (MLflow, AutoML) |
| Pricing Model | Capacity-based (Fabric SKUs) | Pay-as-you-go (serverless) or dedicated | Activity-based | DBU-based (compute + storage) |
| Open Source Support | Limited (Delta-Parquet) | Limited (Spark, SQL) | No | Full (Spark, Python, R, ML frameworks) |
| Governance | Centralized (OneLake, Purview) | Workspace-level | Limited | Workspace-level (Unity Catalog) |
Key Differences
- Fabric vs Synapse: Fabric is a fully managed SaaS (simpler, less configurable), while Synapse offers more control (dedicated SQL pools, Spark clusters).
- ADF vs Synapse Pipelines: Synapse Pipelines = ADF inside Synapse (same engine).
- ADB vs Fabric Spark: ADB has better ML & open-source support, while Fabric Spark is simpler & integrated with Power BI.
When to Use Which
- Microsoft Fabric
- Best for end-to-end analytics in a unified SaaS platform (no infrastructure management).
- Combines data engineering, warehousing, real-time, and BI in one place.
- Good for Power BI-centric organizations.
- Azure Synapse Analytics
- Best for large-scale data warehousing with SQL & Spark processing.
- Hybrid of ETL (Synapse Pipelines), SQL Pools, and Spark analytics.
- More flexible than Fabric (supports open formats like Parquet, CSV).
- Azure Data Factory (ADF)
- Best for orchestrating ETL/ELT workflows (no compute/storage of its own).
- Used for data movement, transformations, and scheduling.
- Often paired with Synapse or Databricks.
- Azure Databricks (ADB)
- Best for advanced analytics, AI/ML, and big data processing with Spark.
- Optimized for Delta Lake (ACID transactions on data lakes).
- Preferred for data science teams needing MLflow, AutoML, etc.
Which One Should You Choose?
- For a fully integrated Microsoft-centric solution → Fabric
- For large-scale data warehousing + analytics → Synapse
- For ETL/data movement → ADF (or Synapse Pipelines)
- For advanced Spark-based analytics & ML → Databricks