May 14, 2023 6 min read

Microsoft Fabric vs Azure Synapse Analytics: A Detailed Comparison

Microsoft Fabric Azure Synapse Data Platform Azure Comparison

With Microsoft Fabric now available, many organizations are evaluating whether to adopt Fabric, stay with Azure Synapse Analytics, or use both. Today, I will provide a detailed comparison to help you make an informed decision.

Architecture Comparison

┌─────────────────────────────────────────────────────┐
│              Azure Synapse Analytics                 │
├─────────────────────────────────────────────────────┤
│  ┌───────────┐  ┌───────────┐  ┌───────────┐       │
│  │Dedicated  │  │Serverless │  │ Spark     │       │
│  │SQL Pool   │  │SQL Pool   │  │ Pool      │       │
│  └─────┬─────┘  └─────┬─────┘  └─────┬─────┘       │
│        │              │              │              │
│        └──────────────┼──────────────┘              │
│                       ▼                             │
│  ┌─────────────────────────────────────────────────┐│
│  │      Azure Data Lake Storage Gen2               ││
│  │          (Separate resource)                    ││
│  └─────────────────────────────────────────────────┘│
│                                                      │
│  + Azure Data Factory (separate)                    │
│  + Power BI (separate)                              │
│  + Azure ML (separate)                              │
└─────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────┐
│              Microsoft Fabric                        │
├─────────────────────────────────────────────────────┤
│  ┌───────────┐  ┌───────────┐  ┌───────────┐       │
│  │Warehouse  │  │Data Eng   │  │ Real-Time │       │
│  │           │  │(Spark)    │  │ Analytics │       │
│  │           │  │           │  │           │       │
│  │ Data      │  │ Data      │  │ Power BI  │       │
│  │ Factory   │  │ Science   │  │           │       │
│  └─────┬─────┘  └─────┬─────┘  └─────┬─────┘       │
│        │              │              │              │
│        └──────────────┼──────────────┘              │
│                       ▼                             │
│  ┌─────────────────────────────────────────────────┐│
│  │                 OneLake                          ││
│  │           (Built-in, unified)                   ││
│  └─────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────┘

Feature Comparison

Capability	Azure Synapse	Microsoft Fabric
Deployment	PaaS (you manage)	SaaS (Microsoft manages)
Storage	ADLS Gen2 (separate)	OneLake (built-in)
SQL Analytics	Dedicated + Serverless pools	Warehouse + Lakehouse SQL endpoint
Spark	Spark pools (provision)	Managed Spark (no provisioning)
ETL/ELT	Synapse Pipelines	Data Factory (integrated)
Streaming	Stream Analytics integration	Real-Time Analytics (KQL)
BI	Power BI (separate)	Power BI (integrated)
ML	Azure ML integration	Built-in Data Science
Networking	VNet, Private Endpoints	Managed (limited customization)
Security	Full Azure RBAC, ACLs	Workspace roles, item permissions

Detailed Comparison

Storage

# Synapse: Manage storage separately
synapse_storage = {
    "type": "Azure Data Lake Storage Gen2",
    "management": "Separate resource, separate billing",
    "access_control": "Azure RBAC + ACLs on folders",
    "cross_service": "Configure linked services for each tool",
    "format": "Parquet, Delta (you choose)"
}

# Fabric: Built-in OneLake
fabric_storage = {
    "type": "OneLake",
    "management": "Automatic, included in capacity",
    "access_control": "Workspace roles (simplified)",
    "cross_service": "Automatic, same identity everywhere",
    "format": "Delta Lake (default)"
}

SQL Analytics

# Synapse Dedicated SQL Pool
synapse_sql = {
    "provisioning": "Manual - choose DWU size",
    "scaling": "Manual or schedule",
    "pausing": "Manual or schedule",
    "pricing": "Per DWU-hour (even when idle if not paused)",
    "features": "Full T-SQL, distributions, indexes"
}

# Synapse Serverless SQL Pool
synapse_serverless = {
    "provisioning": "None",
    "scaling": "Automatic",
    "pausing": "Automatic (pay per query)",
    "pricing": "Per TB processed",
    "features": "T-SQL subset, external tables, OPENROWSET"
}

# Fabric Warehouse
fabric_warehouse = {
    "provisioning": "None (uses capacity)",
    "scaling": "Automatic within capacity",
    "pausing": "N/A (capacity level)",
    "pricing": "Included in capacity",
    "features": "Full T-SQL, automatic optimization"
}

# Fabric Lakehouse SQL Endpoint
fabric_lakehouse_sql = {
    "provisioning": "Automatic with Lakehouse",
    "scaling": "Automatic",
    "pricing": "Included in capacity",
    "features": "Read-only T-SQL, auto-generated from Delta"
}

Spark

# Synapse Spark
synapse_spark = {
    "provisioning": "Create Spark pool, choose node size/count",
    "auto_scale": "Configure min/max nodes",
    "auto_pause": "Configure idle timeout",
    "libraries": "Workspace or pool level packages",
    "clusters": "Dedicated per pool"
}

# Fabric Spark
fabric_spark = {
    "provisioning": "None (managed by Fabric)",
    "auto_scale": "Automatic",
    "auto_pause": "Automatic",
    "libraries": "Environment artifacts",
    "clusters": "Shared, session-based"
}

Migration Considerations

When to Stay with Synapse

stay_with_synapse = {
    "scenarios": [
        "Heavy network isolation requirements (VNet injection)",
        "Complex Azure RBAC requirements at storage level",
        "Large existing Synapse investment",
        "Multi-cloud data platform strategy",
        "Specific Synapse-only features needed"
    ],
    "technical_requirements": [
        "Private endpoints to on-premises",
        "Customer-managed keys at storage level",
        "Complex firewall rules",
        "Specific Spark pool configurations"
    ]
}

When to Move to Fabric

move_to_fabric = {
    "scenarios": [
        "New data platform projects",
        "Microsoft-centric organization",
        "Simplification is a priority",
        "Heavy Power BI usage",
        "Unified team (engineers + analysts)"
    ],
    "benefits": [
        "Simplified administration",
        "Unified security model",
        "OneLake auto-governance",
        "Lower operational overhead",
        "Integrated BI experience"
    ]
}

Hybrid Approach

hybrid_approach = {
    "pattern": "Use both Synapse and Fabric",
    "synapse_for": [
        "Network-isolated workloads",
        "Complex data engineering requiring custom Spark",
        "Existing production workloads"
    ],
    "fabric_for": [
        "New BI and analytics projects",
        "Self-service analytics",
        "Rapid prototyping",
        "Citizen data scientist workloads"
    ],
    "integration": [
        "Shortcuts from Fabric to ADLS (Synapse data)",
        "Power BI reports on both",
        "Shared Azure AD identity"
    ]
}

Migration Path

# Step-by-step migration approach
migration_steps = [
    {
        "phase": 1,
        "name": "Assessment",
        "tasks": [
            "Inventory existing Synapse artifacts",
            "Map Synapse features to Fabric equivalents",
            "Identify gaps and blockers",
            "Estimate capacity requirements"
        ]
    },
    {
        "phase": 2,
        "name": "Pilot",
        "tasks": [
            "Create Fabric trial workspace",
            "Migrate non-critical workload",
            "Test performance and functionality",
            "Train team on Fabric"
        ]
    },
    {
        "phase": 3,
        "name": "Migration",
        "tasks": [
            "Create shortcuts to existing ADLS data",
            "Migrate pipelines to Fabric Data Factory",
            "Convert notebooks (minimal changes)",
            "Recreate warehouse objects",
            "Migrate Power BI to Direct Lake"
        ]
    },
    {
        "phase": 4,
        "name": "Optimization",
        "tasks": [
            "Optimize for Fabric capacity model",
            "Implement Fabric-native features",
            "Decommission Synapse resources",
            "Update governance and security"
        ]
    }
]

Code Migration Examples

Pipeline Migration

// Synapse Pipeline (conceptually similar)
{
  "name": "SynapsePipeline",
  "activities": [
    {
      "name": "CopyData",
      "type": "Copy",
      "linkedServiceName": "AzureBlobStorage",
      "dataset": "BlobDataset"
    }
  ]
}

// Fabric Pipeline (simpler, OneLake native)
{
  "name": "FabricPipeline",
  "activities": [
    {
      "name": "CopyData",
      "type": "Copy",
      "source": {"type": "BlobSource"},
      "sink": {"type": "LakehouseSink", "table": "raw_data"}
    }
  ]
}
// Note: No linked services for OneLake destinations

Notebook Migration

# Synapse Notebook
# Read from ADLS
df = spark.read.format("delta").load("abfss://container@storage.dfs.core.windows.net/path")

# Fabric Notebook
# Read from Lakehouse (simpler path)
df = spark.read.format("delta").table("tablename")
# Or with path
df = spark.read.format("delta").load("Tables/tablename")

Cost Comparison

# Typical medium workload comparison
cost_comparison = {
    "synapse_architecture": {
        "dedicated_pool_dw1000c": 12000,  # Monthly USD
        "serverless_queries": 500,
        "spark_pool": 2000,
        "adls_storage": 200,
        "adf_activities": 300,
        "power_bi_premium_p1": 4995,
        "total": 19995
    },
    "fabric_architecture": {
        "f128_capacity": 9992,  # Monthly USD
        "onelake_storage": 0,   # Included
        "all_workloads": 0,     # Included
        "total": 9992
    },
    "notes": [
        "Fabric often cheaper for unified workloads",
        "Synapse may be cheaper for specialized heavy SQL",
        "Factor in operational overhead savings",
        "Consider pause/resume patterns"
    ]
}

Both Synapse and Fabric are excellent platforms. The choice depends on your specific requirements, existing investments, and organizational priorities.