Top 8 Challenges in Synapse/ADF to Microsoft Fabric Migration

If you’re currently on Azure Synapse or Azure Data Factory (ADF) and looking at Microsoft Fabric, you’ve probably heard the pitch:

“Fabric is unified. Fabric is simpler. Fabric reduces your cloud bill. Fabric is the future.”

All true. But the part nobody tells you?

The migration isn’t plug-and-play. And most teams hit the same roadblocks.

Here are the biggest challenges you should prepare for — based on what architects, engineers, and BI teams are actually discussing on Reddit, Quora, and Microsoft Tech Community forums.

1. Your Existing ADF Pipelines Won’t Move Over Automatically

This is easily the biggest shock. ADF → Fabric does not have a one-click migration path.

• Even though Fabric uses Data Factory pipelines, the engines are different:
• ADF uses Azure Integration Runtime
• Fabric Data Factory uses a new runtime and pipeline engine
• Some connectors behave differently
• Activities aren’t 1:1 identical

Meaning:
You will rebuild, not migrate, a large portion of your ADF pipelines.

If your pipelines are simple (copy data + some mapping):
Migration is easy.

If your pipelines are complex (foreach loops, branching, big parameterization, staging layers):
Expect refactoring.

2. Synapse Notebooks → Fabric Notebooks: They Look Same, But They’re Not

Both use Spark… but not the same Spark cluster.

Common issues:

• Spark configurations don’t map 1:1
• Synapse %run notebook imports work differently
• Mounts and linked services must be re-created
• Library dependencies need reinstalling
• Python versions aren’t always identical
• Some Synapse runtime functions don’t exist in Fabric

Most teams end up rewriting 15–25% of notebook code.

3. No Dedicated SQL Pools in Fabric → Modeling Mindset Must Change

This is a big conceptual shift.

In Synapse:

• You used dedicated SQL pools or serverless SQL
• You probably used stored procs, user-defined functions, and CTAS patterns
• You may have materialized tables inside the warehouse itself

In Fabric:

• You’re now writing to Lakehouse or Fabric Warehouse backed by Delta
• You lose some T-SQL capabilities
• You gain new Lakehouse/Spark patterns
• You must rethink star schemas, materialization, and governance

Teams that don’t adjust their modeling approach often struggle early.

4. OneLake Shortcuts Are Powerful — But Need a New Data Architecture Mindset

Shortcuts are amazing (seriously). But they force you to think differently:

• No more ETL-ing data into 10 different stores
• No more “data copy culture”
• No more multiple bronze layers across workspaces

However, some teams misuse shortcuts and end up with:

• Circular references
• Wrong workspace scoping
• Performance bottlenecks
• Confusing lineage

Architects must re-learn how to design with a single logical lake.

5. Security, Access, and Governance Work Differently

Synapse + ADF security is role-based and mostly Azure-native.

• Fabric’s security model is:
• Workspace based
• Capacity based
• Item-level
• Purview-powered
• Deeply integrated with OneLake

This means:

• More control
• More governance
• More configuration work

Teams often underestimate the amount of access redesign required.

6. Learning the Fabric Capacity Model (This Confuses Everyone)

Fabric capacities (F SKUs) don’t behave like Synapse DWUs or ADF per-activity billing. In Fabric, everything consumes the same bucket:

• Pipelines
• SQL warehouse queries
• Spark notebooks
• Power BI
• Real-time analytics

Most organizations initially:

• Under-provision
• Run out of capacity
• Misinterpret throttling
• Overload the engine

You must learn capacity planning, or you’ll overspend or slow down. Having structured Fabric guidance or hands-on training can help your team understand workloads, optimize pipelines, and avoid costly mistakes.

7. Monitoring & Observability Are Completely Different

ADF has great pipeline monitoring. Synapse has decent SQL & Spark monitoring.

Fabric uses:

• Capacity metrics
• Item metrics
• Workspace usage
• Monitoring hub

The information is there… but not where you expect it. Teams often feel “blind” the first few weeks.

8. Mindset Shift: Fabric Is NOT Synapse 2.0 — It’s a Unified Analytics Platform

This may be the biggest challenge of all. If you try to implement Fabric the same way you architected:

• Synapse
• ADF
• Snowflake
• Databricks

…you will struggle.

Fabric requires:

• Unified storage architecture
• Distributed modeling ownership
• Central governance + decentralized execution
• Spark-first lakehouse mindset
• More collaboration between DE + BI teams

It’s a new ecosystem — not an upgrade.

Final Thoughts: Should You Migrate?

Yes — but with a plan. Fabric is powerful, future-proof, and enterprise-ready. But migrating without a clear adoption roadmap? That’s where teams burn time and budget.

If you’re moving from Synapse/ADF → Fabric, you need:

• A migration plan
• A new data architecture blueprint
• Teams trained in Fabric engineering
• Proper capacity planning
• Governance setup before development

Do those right… and migration becomes smooth.

Editor’s Note

If your organization is planning a Fabric migration, investing in proper Fabric Data Engineering training is one of the smartest moves you can make. Most migration issues happen not because engineers lack experience — but because Fabric follows a completely different architecture and engineering approach.

Our Fabric Data Engineering program covers:

• End-to-end ADF → Fabric pipeline conversion
• Synapse to Fabric notebook migration
• Lakehouse modeling in OneLake
• Spark optimization in Fabric
• Enterprise workspace + governance setup

This can save you months of trial and error during migration.
 

Microsoft Fabric