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Fabric vs Databricks vs Synapse¶

Last Updated: 2026-05-05 | Version: 1.0.0

TL;DR¶

Choose Fabric when you want a unified SaaS analytics platform with integrated Lakehouse, Warehouse, Power BI, and Real-Time Intelligence under one capacity. Choose Databricks when your workload is ML/AI-heavy, multi-cloud, or requires Unity Catalog governance already in place. Keep Synapse Analytics for existing dedicated SQL pool investments, but plan migration to Fabric for new workloads.

When This Question Comes Up¶

• Greenfield project selecting a primary analytics platform on Azure
• Existing Synapse workspace evaluating migration to Fabric
• Organization uses Databricks on AWS/GCP and is considering Azure consolidation
• Team needs to justify Fabric adoption vs expanding existing Databricks investment
• Multi-cloud strategy requires interoperability between Fabric and external platforms

Decision Flowchart¶

flowchart TD
    START([New analytics platform decision]) --> EXISTING{Existing platform<br/>investment?}

    EXISTING -->|Greenfield - no<br/>existing platform| WORKLOAD
    EXISTING -->|Synapse Analytics| SYNAPSE_CHECK{Dedicated SQL pools<br/>or Serverless only?}
    EXISTING -->|Databricks| DB_CHECK{Multi-cloud<br/>requirement?}
    EXISTING -->|Other / on-prem| WORKLOAD

    SYNAPSE_CHECK -->|Dedicated SQL pools<br/>active workloads| SYNAPSE_KEEP[Keep Synapse Dedicated<br/>+ evaluate Fabric migration]
    SYNAPSE_CHECK -->|Serverless / Spark only| FABRIC_MIGRATE[Migrate to Fabric<br/>Lakehouse + Warehouse]

    DB_CHECK -->|Yes - AWS, GCP,<br/>Azure all needed| DB_MULTI[Databricks<br/>multi-cloud Unity Catalog]
    DB_CHECK -->|No - Azure only| WORKLOAD

    WORKLOAD{Primary workload?} --> WL_ANALYTICS{Analytics + BI<br/>dominant?}

    WL_ANALYTICS -->|Yes - dashboards,<br/>reports, KPIs| FABRIC[Fabric<br/>unified SaaS platform]
    WL_ANALYTICS -->|No| WL_ML{ML / AI<br/>dominant?}

    WL_ML -->|Yes - training,<br/>MLOps, serving| ML_SCALE{Scale of ML<br/>operations?}
    WL_ML -->|No| WL_STREAMING{Real-time<br/>streaming dominant?}

    ML_SCALE -->|Enterprise MLOps,<br/>GPU clusters, LLM fine-tuning| DATABRICKS[Databricks<br/>ML-optimized platform]
    ML_SCALE -->|Lightweight ML,<br/>AutoML, scoring| FABRIC

    WL_STREAMING -->|Yes| FABRIC
    WL_STREAMING -->|No - general<br/>data engineering| TEAM{Team's primary<br/>ecosystem?}

    TEAM -->|Microsoft 365,<br/>Power Platform, Azure| FABRIC
    TEAM -->|Open source,<br/>multi-cloud, Spark-heavy| DATABRICKS

    FABRIC --> FABRIC_DONE[Fabric<br/>OneLake + Spark + Power BI<br/>+ Real-Time Intelligence]
    DATABRICKS --> DB_DONE[Databricks<br/>Unity Catalog + MLflow<br/>+ Delta Sharing]

    style FABRIC_DONE fill:#4CAF50,color:#fff
    style DB_DONE fill:#FF5722,color:#fff
    style SYNAPSE_KEEP fill:#2196F3,color:#fff
    style FABRIC_MIGRATE fill:#4CAF50,color:#fff
    style DB_MULTI fill:#FF5722,color:#fff

Fabric¶

When¶

• Primary workload is analytics and BI with Power BI as the consumption layer
• Organization is Microsoft-first (M365, Power Platform, Azure)
• Unified governance under a single capacity model is valued over best-of-breed components
• Real-Time Intelligence (Eventstream, Eventhouse, KQL) is needed
• Team wants a SaaS experience with minimal infrastructure management

Why¶

• Single platform: Lakehouse, Warehouse, Power BI, Real-Time Intelligence, Data Activator
• OneLake provides unified storage with automatic Delta Lake format
• Direct Lake connectivity eliminates Import refresh overhead for Power BI
• Integrated governance through Microsoft Purview
• Capacity-based pricing consolidates compute billing

Tradeoffs¶

Anti-patterns¶

• Running large-scale ML training (GPU clusters, LLM fine-tuning) on Fabric Spark instead of Databricks
• Choosing Fabric solely for Spark workloads when the team has deep Databricks expertise and Unity Catalog
• Ignoring Fabric's capacity limits for concurrent users -- under-provisioning leads to throttling
• Treating Fabric as "just a Lakehouse" and not leveraging Real-Time Intelligence, Data Activator, or Copilot

Databricks¶

When¶

• ML/AI is the dominant workload: model training, MLflow experiment tracking, GPU clusters
• Multi-cloud strategy requires consistent platform across AWS, GCP, and Azure
• Unity Catalog is already the governance layer for data and ML assets
• Team is deeply invested in open-source Spark ecosystem and Delta Sharing

Why¶

• Purpose-built for ML/AI: GPU-optimized clusters, MLflow, Feature Store, Model Serving
• Multi-cloud: identical experience on AWS, GCP, Azure
• Unity Catalog provides unified governance across data, ML models, and notebooks
• Delta Sharing enables secure cross-organization data sharing without copying
• Mature Spark ecosystem with extensive third-party integrations

Tradeoffs¶

Anti-patterns¶

• Using Databricks as a BI platform (no native dashboarding equivalent to Power BI + Direct Lake)
• Running Databricks on Azure solely for Spark when Fabric Lakehouse provides equivalent compute
• Ignoring OneLake interoperability -- Databricks can read/write OneLake via ABFS shortcuts
• Maintaining separate Databricks and Fabric environments without Delta Sharing or shortcut integration

Synapse Analytics (Legacy)¶

When¶

• Active dedicated SQL pool investments with complex migrations that cannot move to Fabric yet
• Existing Synapse Serverless SQL pools (consider migrating to Fabric Lakehouse SQL endpoint)
• Synapse Spark pools with established CI/CD (evaluate Fabric Spark with fabric-cicd)
• Regulatory or contractual lock-in to existing Synapse infrastructure

Why¶

• Dedicated SQL pools provide guaranteed compute isolation for large-scale DW workloads
• Existing Synapse pipelines and linked services may be complex to migrate immediately
• Some compliance frameworks may have Synapse specifically in their authorization boundary

Tradeoffs¶

Anti-patterns¶

• Starting new greenfield projects on Synapse instead of Fabric (Synapse is in maintenance mode)
• Maintaining Synapse Serverless when Fabric Lakehouse SQL endpoint provides equivalent functionality
• Not planning a migration path -- Synapse Spark and pipelines have direct Fabric equivalents
• Running Synapse and Fabric in parallel without a consolidation roadmap

Quick Comparison¶

Related Links¶

• Fabric IQ -- AI-powered Fabric assistant
• Migration Patterns -- Platform migration strategies
• OneLake Shortcuts -- Cross-platform data access
• Iceberg Interoperability -- Open table format compatibility
• Capacity Planning -- Right-sizing Fabric capacity
• Spark Environments -- Fabric Spark configuration
• AutoML -- Fabric-native ML capabilities