Azure SQL Database Data Sync Retirement: Migration Scenarios and Recommended Alternatives

Why Azure SQL Data Sync Retirement Matters

Azure SQL Data Sync relies on:

• Triggers
• Metadata tables
• Hub-and-spoke topology

While functional, this architecture introduces complexity, performance overhead, and operational risks, especially as data volumes and workloads grow.

Microsoft’s long-term direction favors scalable, resilient, and observable data integration services, such as Azure Data Factory (ADF) and event-driven replication patterns.

If you are currently using Data Sync, planning a migration early is strongly recommended.

Official guidance:

https://learn.microsoft.com/azure/azure-sql/database/sql-data-sync-data-sql-server-sql-database 

Sample Customer Scenario

Let’s consider a real scenario commonly seen in the field:

• 4 Azure SQL Databases
• Subscription: Contoso-DEV
• Current topology: Azure SQL Data Sync
• Target state: Consolidate all data into one Azure SQL Database
• Environment flow: DEV → UAT → PROD
• Database tiers: Standard (S0 / S1)
• Size: Below 250 GB per database
• Key requirements:
  • Minimal data loss
  • Quick replication
  • Azure-native and supported replacement
  • Clear operational model

Migration Design Considerations

Before selecting a tool, several factors must be evaluated:

• ✅ Latency tolerance (near real-time vs scheduled sync)
• ✅ Write patterns (conflicts, bidirectional vs unidirectional)
• ✅ Schema compatibility
• ✅ Operational overhead
• ✅ Long-term supportability

For most consolidation scenarios, unidirectional replication (many → one) provides the best balance of simplicity and reliability.

Diagram 1: Current State – Azure SQL Data Sync (Before Retirement)

This diagram represents the existing topology, where multiple databases are synchronized using Azure SQL Data Sync into a single consolidated database.

Characteristics

• Trigger‑based synchronization
• Additional metadata tables
• Limited observability
• Service approaching retirement

Diagram 2: Target State – Azure Data Factory Based Consolidation

This diagram shows the recommended replacement architecture using Azure Data Factory.

Advantages

• No triggers or sync metadata tables
• Parallel ingestion
• Built‑in retry, monitoring, and alerting
• Fully supported and future‑proof

Diagram 3: Incremental Replication Logic (ADF)

This diagram explains how minimal data loss is achieved using incremental replication.

Key Points

• No continuous connection required
• Typical RPO: 1–5 minutes
• Safe restart after failures

Diagram 4: DEV → PROD Migration Flow

This diagram highlights the recommended rollout approach starting with POC in DEV.

Best Practices

• Build once, reuse across environments
• Parameterize connection strings
• Enable monitoring before PROD cutover

Recommended Alternatives to Azure SQL Data Sync

✅ Option 1: Azure Data Factory (ADF) – Primary Recommendation

Azure Data Factory provides a fully supported and scalable replacement for Data Sync when consolidating databases.

Architecture Overview

• One pipeline per source database
• Initial full load
• Incremental replication using:
  • Change Tracking, or
  • CDC (if applicable), or
  • Watermark columns (ModifiedDate / identity)

Why ADF?

• Microsoft’s strategic data integration platform
• Built-in monitoring and retry logic
• Parallel ingestion
• Schema mapping and transformation support

📌 Best fit when:

• You need consolidation
• Near real‑time (minutes) is acceptable
• You want a future‑proof design

📘 References:

• https://learn.microsoft.com/azure/data-factory/copy-activity-overview 
• https://learn.microsoft.com/azure/data-factory/incremental-copy-overview 
• https://learn.microsoft.com/azure/data-factory/connector-azure-sql-database 

⚠️ Option 2: SQL Transactional Replication (Limited Use)

Transactional replication can still work in narrow scenarios, but:

• Adds operational complexity
• Limited flexibility for schema changes
• Not recommended for new designs

📘 Reference:

• https://learn.microsoft.com/azure/azure-sql/database/replication-to-sql-database 

🧭 Option 3: Azure SQL Managed Instance Link (Future‑Facing)

If your long-term roadmap includes Azure SQL Managed Instance, the MI Link feature enables near real-time replication.

However:

• Not applicable if your target remains Azure SQL Database
• Requires infrastructure change

📘 Reference:

• https://learn.microsoft.com/azure/azure-sql/managed-instance/link-feature 

Recommended Migration Approach (DEV → PROD)

Phase 1 – Assessment

• Review schema overlaps and key conflicts
• Identify identity and primary key strategies
• Confirm availability of:
  • Change Tracking
  • ModifiedDate / watermark columns

📘 Change Tracking:

• https://learn.microsoft.com/sql/relational-databases/track-changes/about-change-tracking-sql-server 

Phase 2 – Initial Seeding (DEV)

• Use ADF Copy Activity for full loads
• Ingest each source DB into:
  • Dedicated schemas, or
  • Logical partitions
• Validate:
  • Row counts
  • Referential integrity
  • Performance impact

Phase 3 – Incremental Replication

• Enable incremental pipelines
• Recommended frequency: every 1–5 minutes
• Use parallelism for scalability
• Simulate Data Sync behavior without triggers

Phase 4 – Cutover

• Optional short write freeze
• Final delta sync
• Application validation
• Promote pipelines to PROD

Data Loss and Performance Expectations

Final Recommendation Summary

✅ Azure Data Factory with initial full load + incremental replication
✅ Azure-native, strategic, and supported
✅ Ideal for Data Sync retirement scenarios
✅ Scales from DEV to PROD with minimal redesign

 

Azure SQL Data Sync retirement is an opportunity—not a setback.

With services like Azure Data Factory, customers can move toward:

• Better observability
• Cleaner architectures
• Easier production operations
• Long-term platform alignment

If you are still relying on Azure SQL Data Sync, now is the right time to assess, plan, and migrate.

Helpful Resources

• Azure SQL Data Sync overview
  https://learn.microsoft.com/azure/azure-sql/database/sql-data-sync-data-sql-server-sql-database 
• Azure Data Factory incremental copy
  https://learn.microsoft.com/azure/data-factory/incremental-copy-overview 
• Azure SQL change tracking
  https://learn.microsoft.com/sql/relational-databases/track-changes/about-change-tracking-sql-server