1 min read
Microsoft Fabric vs Azure Synapse Analytics: A Detailed Comparison
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.