Azure Synapse vs Microsoft Fabric: Migration, Performance, and a Practical Path Forward

If you’re running analytics on Azure today, you’ve likely asked: Should we stay with Azure Synapse Analytics, or migrate to Microsoft Fabric? And if we move, how do we preserve performance, control costs, and avoid a risky rewrite?

This guide cuts through the noise with a practical, no-fluff comparison of Azure Synapse vs Microsoft Fabric—from architecture and performance to pricing and governance—plus a step-by-step migration blueprint you can adapt to your environment.

Along the way, you’ll find actionable tips, common pitfalls to avoid, and a decision framework to help you choose the right path for your data platform.

Tip: If you’re new to the lakehouse concept powering Fabric, this overview of the modern stack is a great primer: Data Lakehouse Architecture: The Future of Unified Analytics.

Quick Definitions: What Each Platform Does Best

What is Azure Synapse Analytics?

Azure Synapse is a unified analytics service with multiple engines and tools under one roof:

• Dedicated SQL pools (MPP data warehouse)
• Serverless SQL (ad hoc queries on data in data lakes)
• Apache Spark pools (data engineering and data science)
• Pipelines built on Azure Data Factory (ETL/ELT orchestration)
• Synapse Studio for end-to-end development

Best when:

• You need fine-grained control over infrastructure.
• You’re invested in ADLS Gen2, Synapse pipelines, and dedicated SQL pools.
• You need specialized workloads like high-scale MPP warehousing or integrated ADF/Synapse orchestration.

What is Microsoft Fabric?

Microsoft Fabric is an integrated, SaaS-first data platform that unifies storage, compute, and analytics experiences in a single capacity model. It’s built on OneLake and the lakehouse pattern (Delta/Parquet) and includes:

• Data Engineering (Spark)
• Data Factory (pipelines and dataflows gen2)
• Data Warehouse (SQL-based lakehouse engine)
• Real-Time Intelligence (event streaming, KQL)
• Data Science
• Power BI and Direct Lake for blazing-fast BI

Best when:

• You want an end-to-end, SaaS-managed platform with fewer moving parts.
• You’re standardizing on lakehouse storage (Delta) and Power BI.
• You want faster time-to-value, simpler governance/lineage, and a single capacity model for cost control.

For a step-by-step strategy to integrate both or plan a migration, check this deep dive: Azure Synapse and Microsoft Fabric: A Practical Roadmap to Integrate and Modernize Your Data Ecosystem.

Azure Synapse vs Microsoft Fabric: The Big Differences That Matter

1) Architecture and Storage

• Synapse: Modular, multi-engine platform running on Azure infrastructure you configure. Storage is typically ADLS Gen2.
• Fabric: SaaS-first, lakehouse-native with OneLake as the logical data fabric. Uses Delta/Parquet by default and supports shortcuts to external data (like ADLS Gen2) to reduce data movement.

Why it matters: Fabric’s OneLake and shortcuts simplify governance and migration. Synapse offers more “Lego blocks” for custom setups.

2) Compute Model and Performance

• Synapse Dedicated SQL: MPP engine with distribution keys, partitions, and workload management—excellent for classic warehousing at scale.
• Synapse Serverless SQL: On-demand queries over data in the lake—great for exploration and cost efficiency.
• Fabric Warehouse & Lakehouse: Optimized for SQL on Delta with decoupled compute; tight integration with Power BI and Direct Lake for high-performance analytics without heavy imports.

Why it matters: For BI at scale, Fabric’s Direct Lake can remove the usual import/refresh bottlenecks. For heavy MPP workloads with specific tuning requirements, Synapse Dedicated SQL remains strong.

3) Data Integration and Orchestration

• Synapse: Pipelines are powered by Azure Data Factory; broad connector ecosystem and Integration Runtime options.
• Fabric: Data Factory experience is integrated in the same workspace and capacity. Many ADF patterns are available, and more parity continues to arrive.

Why it matters: Fabric streamlines integration into the same SaaS surface, while Synapse/ADF still offers mature, enterprise-grade patterns you may rely on.

Pro tip: If you’re standardizing ingestion ahead of a migration, a pattern like Metadata-Driven Ingestion in Azure Data Factory reduces rework when you shift orchestration to Fabric later.

4) BI and the Semantic Layer

• Synapse + Power BI: Mature integration; typically import or DirectQuery modes.
• Fabric + Power BI: Direct Lake enables near-real-time reporting on lakehouse data without heavy refresh cycles—often a performance game-changer.

5) Governance, Security, and Lineage

• Synapse: Azure-native RBAC, private networking, Purview integration for catalog/lineage.
• Fabric: Workspace-level governance, OneLake security, Entra ID integration, and built-in lineage across pipelines, notebooks, models, and reports.

Why it matters: Fabric reduces context switching in governance and lineage; Synapse gives you granular network and resource controls.

6) Pricing and TCO

• Synapse: Pay for the resources you allocate (DWUs, Spark nodes) and storage separately.
• Fabric: Capacity-based pricing shared across experiences (engineering, warehousing, BI).

Guideline: Fabric can simplify and consolidate costs—especially if Power BI is central. Synapse can be optimal when you need to precisely control specific engines and scale tiers.

7) Maturity and Roadmap

• Synapse: A stable, Azure-native analytics workhorse with a long track record.
• Fabric: Rapidly evolving, with strong investment from Microsoft; feature parity with Synapse/ADF is broad and still expanding.

Should You Migrate? A Decision Framework

Consider a move to Fabric if:

• Power BI is your primary analytics interface and you want Direct Lake performance.
• Your team prefers a SaaS platform that reduces operational overhead.
• You’re standardizing on Delta/Parquet and a lakehouse model.
• You want unified capacity and simpler cross-domain governance.

Consider staying on Synapse (for now) if:

• You rely on specific Synapse Dedicated SQL features or intricate ADF Integration Runtime scenarios not yet mirrored in Fabric.
• You require highly customized networking/topology controls.
• Your platform is running efficiently with predictable costs and SLAs.

Consider a hybrid approach if:

• You want to adopt Fabric for BI and new workloads while keeping core Synapse pipelines or MPP workloads running during transition.
• You plan to use OneLake shortcuts to avoid moving large data sets immediately.

A Practical Migration Blueprint (Minimize Risk, Maximize Reuse)

Use this as a modular plan—run a pilot first, then scale.

1) Pre-Assessment

• Clarify business drivers: performance, cost, governance, developer experience, time-to-insight.
• Define success metrics: refresh SLAs, query latency targets, cost envelopes, adoption KPIs.

2) Inventory and Dependency Mapping

• Catalog Synapse assets: Dedicated/Serverless SQL, tables, views, materialized views, notebooks, pipelines, triggers, linked services, credentials, and Power BI datasets/reports.
• Map sources/targets, security groups, data products, and lineage (Purview helps).

3) Storage Strategy with OneLake

• Decide what moves to OneLake vs what stays in ADLS Gen2 (use shortcuts to reduce copy operations).
• Standardize on Delta for analytics-ready data.

4) Choose Target Services in Fabric

• Dedicated SQL pool → Fabric Warehouse
• Serverless SQL → Lakehouse + SQL analytics endpoint
• Spark notebooks → Data Engineering (Spark)
• ADF/Synapse pipelines → Fabric Data Factory (dataflows gen2 where appropriate)
• Synapse Data Explorer/Kusto → Fabric Real-Time Intelligence (KQL DB)
• Power BI datasets → Direct Lake semantic models when possible

5) Schema and Data Model Alignment

• Normalize data types and naming conventions.
• Revisit star schemas for BI and adopt lakehouse medallion layering (bronze/silver/gold) for reliability and performance.

6) Pipeline and Orchestration Migration

• Recreate pipelines in Fabric Data Factory; refactor complex IR scenarios where needed.
• Parameterize where possible to keep pipelines reusable and environment-agnostic.

7) Security and Governance

• Map Entra ID groups/roles to Fabric workspaces.
• Validate policies, sensitivity labels, and row/column-level security across the stack.
• Align Purview/catalog lineage with Fabric’s built-in lineage.

8) Power BI and Direct Lake

• Migrate or rebuild semantic models using Direct Lake to remove refresh bottlenecks.
• Implement composite models where hybrid sources are required.

9) Performance and Cost Validation

• Benchmark representative workloads (ETL, BI queries, ad hoc exploration).
• Right-size Fabric capacity, test concurrency, and evaluate autoscale strategies.
• Compare monthly run rates with Synapse baselines.

10) Pilot, Cutover, and Rollback

• Start with a well-scoped domain or data product.
• Run in parallel for a full cycle to ensure parity.
• Execute cutover during low-traffic windows with a clear rollback plan.

For a more detailed migration journey, see: Azure Synapse and Microsoft Fabric: A Practical Roadmap to Integrate and Modernize Your Data Ecosystem.

Performance Tuning Tips (Fabric and Synapse)

Fabric Warehouse and Lakehouse

• Store analytics data in Delta; partition by common filters (e.g., date, region).
• Optimize and vacuum Delta tables regularly to compact small files.
• Use Direct Lake for BI; validate model sizes and cardinality to keep it responsive.
• Use materialized semantics judiciously (aggregations, calculated tables) when needed.

Synapse Dedicated SQL

• Choose distribution keys carefully; avoid data skews.
• Partition large fact tables and leverage clustered columnstore indexes.
• Use CTAS for transformations; leverage workload groups and query priorities.
• Cache hot datasets or use result-set caching where appropriate.

Spark (Both Platforms)

• Read/write in columnar formats (Parquet/Delta) with snappy/zstd compression.
• Cache only when reused multiple times; monitor memory and shuffle costs.
• Push filters and projections down to the source; avoid wide transformations early.

Power BI (Both Platforms)

• Model first: conformed dimensions, proper relationships, and measure design.
• Use aggregations and composite models strategically.
• Validate DAX performance; limit high-cardinality columns in core visuals.

Cost Optimization Essentials

• Fabric
• Right-size capacity to actual concurrency and workloads.
• Separate dev/test from prod capacities; pause non-prod when idle.
• Use Direct Lake to reduce heavy refresh cycles and compute costs.

• Synapse
• Start/stop dedicated SQL pools outside business hours.
• Use serverless for exploration; avoid landing exploratory workloads on dedicated pools.
• Compress and tier storage; delete or archive cold data.

• Cross-Platform
• Eliminate redundant pipelines and models during migration.
• Embrace Delta/Parquet to avoid expensive conversions.
• Track cost per domain or data product to align platform use with business value.

Common Pitfalls (And How to Avoid Them)

• Big-bang migrations: Start with a pilot domain to de-risk and prove value.
• Rebuilding everything as-is: Refactor to lakehouse-friendly patterns instead of lifting and shifting.
• Data type drift: Align to a canonical schema early; validate during cutover.
• Ignoring semantics: BI performance is a data modeling problem as much as an engine problem.
• Over-provisioning: Measure, tune, and right-size Fabric capacities (or Synapse pools) based on real workloads.
• Underestimating governance: Map security groups and sensitivity labels; validate lineage end-to-end before go-live.

Timelines and Team Roles

• Pilot: 6–10 weeks for a focused domain (ingestion, transformations, semantic model, BI).
• Scale-up: 3–6 months to cover multiple domains with shared patterns.
• Core roles: Platform owner, data engineer, BI modeler, data product owner, security/governance lead, FinOps/capacity manager.

Related Reading You May Find Helpful

• Understand the lakehouse foundation behind Fabric: Data Lakehouse Architecture: The Future of Unified Analytics
• Plan your integration or migration journey: Azure Synapse and Microsoft Fabric: A Practical Roadmap to Integrate and Modernize Your Data Ecosystem
• Standardize ingestion now to simplify migration later: Metadata-Driven Ingestion in Azure Data Factory

FAQs: Azure Synapse vs Microsoft Fabric

1) Is Microsoft Fabric replacing Azure Synapse?

Not immediately. Synapse continues to be supported and widely used. Fabric is the modern, SaaS-first evolution of Microsoft’s analytics vision with strong integration across engineering, warehousing, and BI. Many teams will run hybrid for a period, adopting Fabric for BI and new workloads while core Synapse workloads continue.

2) When does it make sense to migrate from Synapse to Fabric?

Common triggers include:

• You want Direct Lake for faster, simpler BI performance.
• You’re ready to standardize on lakehouse storage (Delta) and reduce imports.
• You want centralized governance, lineage, and capacity-based cost control.
• You’re consolidating multiple tools into a single platform surface.

3) Can I use my existing ADLS data without copying it into Fabric?

Yes. Fabric OneLake supports shortcuts to external storage (such as ADLS Gen2). This helps you adopt Fabric’s experiences while minimizing data movement during transition.

4) How does Direct Lake improve Power BI performance?

Direct Lake lets Power BI read datasets directly from the lakehouse in OneLake (Delta), avoiding frequent heavy refreshes or DirectQuery latency. The result is faster time-to-insight and simpler refresh architectures for many scenarios.

5) What are the main performance levers in Fabric compared to Synapse?

• Fabric: Delta table optimization, partitioning, Direct Lake, right-sized capacity, and efficient semantic modeling.
• Synapse: Distribution strategy, partitioning, materialized views, workload groups, CTAS, and columnstore tuning.

In both, data model quality (star schemas, proper relationships, and measures) is crucial.

6) How should we estimate Fabric capacity?

Start by benchmarking representative workloads (ingestion, transformations, BI concurrency) in a pilot. Use telemetry to size capacity for peak concurrency, then refine. Separate non-prod capacity to pause when idle and avoid compute waste.

7) Are all ADF/Synapse pipeline features available in Fabric?

Fabric’s Data Factory covers many common ADF patterns and keeps adding more. Some advanced Integration Runtime scenarios may need rethinking. Inventory your pipelines early and prototype the complex ones first.

8) What’s the best migration approach: big bang or iterative?

Iterative. Begin with a single domain or data product, validate performance and costs, and codify patterns (naming standards, ingestion templates, medallion layers). Then scale. Run parallel for at least one full business cycle before cutover.

9) How do governance and security differ?

Synapse leans on Azure-native controls (RBAC, networking, Purview). Fabric centralizes governance and lineage in the same SaaS platform, with Entra ID integration and OneLake security. The best approach is to document policies early and test end-to-end with real identities and sensitivity labels.

10) Do we need to convert everything to Delta?

For analytics workloads in Fabric, Delta is the recommended standard. It enables performance features, ACID transactions, and simpler governance. You can keep external stores and use shortcuts during transition, but define a clear end-state for Delta-based analytics.

Bottom line: If Power BI and unified analytics are central to your strategy, Microsoft Fabric likely accelerates your roadmap with better performance, simpler governance, and consolidated costs. If you’re running specialized MPP workloads or deeply embedded ADF patterns, a staged migration or hybrid approach can give you the best of both worlds—without risking reliability or budget.