Microsoft Fabric & Azure Databricks
Microsoft Fabric is the modern analytics platform. Azure Databricks is the big data powerhouse. Learn what each does and when to choose one over the other.
The two big analytics platforms
Microsoft Fabric is the all-in-one analytics kitchen. Azure Databricks is the specialised gourmet workshop.
Fabric has everything in one room — oven, fridge, dishwasher, dining table. You can prepare food, cook, plate, and serve without leaving the kitchen. Databricks is a professional-grade workshop for expert chefs — more powerful tools for complex dishes, but you bring your own plates and dining room.
Most teams start with Fabric. Teams with advanced data engineering or data science needs may use Databricks alongside it.
Microsoft Fabric
Fabric is Microsoft’s unified analytics platform — a single SaaS service that replaces the need for multiple separate analytics tools.
What’s inside Fabric:
| Workload | What It Does |
|---|---|
| Data Factory | Build data pipelines (ETL/ELT) with 200+ connectors |
| Data Engineering | Apache Spark for large-scale data processing |
| Data Warehouse | SQL-based analytical queries on structured data |
| Real-Time Intelligence | Stream processing and real-time analytics |
| Data Science | Machine learning experiments and model training |
| Power BI | Dashboards, reports, and data visualisation |
The unifying layer: OneLake All Fabric workloads store data in OneLake — a single, organisation-wide data lake. No data duplication between services. A data engineer writes to OneLake, and a data analyst queries the same data in Power BI — no copies, no movement.
Priya’s FreshMart uses Fabric for everything:
- Data Factory pipelines pull nightly data from 50 stores
- Data Engineering cleans and transforms using Spark notebooks
- Data Warehouse serves structured queries
- Power BI dashboards connect directly to the warehouse
- All data lives in one OneLake — no silos
Azure Databricks
Databricks is an Apache Spark-based analytics platform — jointly developed by Microsoft and Databricks, Inc. It runs on Azure infrastructure but is managed by Databricks.
Key capabilities:
- Collaborative notebooks — Python, SQL, Scala, R in shared workspaces
- Apache Spark — distributed data processing for massive datasets
- Delta Lake — ACID transactions on data lakes
- Machine learning — MLflow for experiment tracking, model management
- Unity Catalog — data governance across all workspaces
When Databricks excels:
- Advanced data engineering with complex Spark jobs
- Data science and ML model training at scale
- Multi-cloud environments (also runs on AWS and GCP)
- Teams with deep Spark/Python expertise
| Feature | Microsoft Fabric | Azure Databricks |
|---|---|---|
| Type | SaaS (fully managed) | PaaS (Spark-based) |
| Audience | All data roles (analysts to engineers) | Data engineers and data scientists |
| Storage | OneLake (built-in, unified) | ADLS Gen2 (external, you configure) |
| Power BI | Integrated natively | Connect externally |
| Spark | Included (Fabric Spark) | Core strength (Databricks Spark) |
| Multi-cloud | Azure only | Azure, AWS, GCP |
| Learning curve | Lower (guided experiences) | Higher (requires Spark knowledge) |
| Best for | End-to-end analytics in one platform | Advanced data engineering and ML |
Can you use both together?
Absolutely — and many organisations do. A common pattern:
- Databricks for complex data engineering and ML model training (where Spark expertise shines)
- Fabric for data warehousing, real-time analytics, and Power BI reporting (where the integrated experience shines)
- OneLake shortcuts connect Fabric to Databricks storage, so data flows between them without copying
Fabric and Databricks aren’t competitors for every scenario — they complement each other.
What about Azure Synapse Analytics?
You may see Azure Synapse Analytics mentioned in older materials. Synapse was Microsoft’s previous unified analytics service — it combined SQL pools, Spark pools, and data integration.
Microsoft Fabric is the newer unified analytics platform, positioned as the preferred choice for most new analytics projects. Fabric takes the same ideas (unified analytics, SQL + Spark, integrated pipelines) and delivers them as a simpler SaaS experience with OneLake. Some Synapse features (like dedicated SQL pools) still exist for existing workloads.
For the DP-900 exam, focus on Fabric as the current platform. You may see Synapse mentioned in passing but Fabric is what’s tested.
Exam tip: Fabric vs Databricks selection
- “End-to-end analytics platform” → Fabric
- “Unified storage with OneLake” → Fabric
- “Power BI integrated natively” → Fabric
- “Advanced Spark-based data engineering” → Databricks
- “Machine learning at scale” → Databricks
- “Multi-cloud (Azure + AWS + GCP)” → Databricks
- “SaaS with minimal setup” → Fabric
Flashcards
Knowledge check
FreshMart wants a single platform for data pipelines, warehousing, real-time analytics, and Power BI dashboards — with minimal infrastructure management. Which service?
A data science team needs to train machine learning models using Apache Spark across massive datasets, and they also need the platform to work on AWS (not just Azure). Which service?
Next up: Batch vs Streaming: Two Speeds of Data — not all data arrives the same way.