Introduction

Migrating a database from an on-premises or hosted environment to Microsoft Azure is a strategic move toward scalability, flexibility, and cost efficiency. Successfully navigating this transition requires careful planning and an awareness of common pitfalls.

However, without careful planning and optimization, this migration can lead to performance issues, cost overruns, and data inconsistencies – the very problems we were hoping to eliminate.

Whether we’re moving to Azure SQL Database, Managed Instance, or Azure SQL Virtual Machines, avoiding common pitfalls is critical for a successful cloud transition.

Here are the 12 most common mistakes to avoid when migrating your database to Azure and how to get it right.

1. Skipping the Pre-Migration Assessment

Mistake:
Failing to thoroughly assess your existing database, application dependencies, and server infrastructure for compatibility with your chosen Azure database service (like Azure SQL Database, Azure SQL Managed Instance, or Azure Virtual Machines).

Why it’s bad:

• Some features (e.g., SQL CLR, linked servers, cross-database queries) aren’t supported in certain Azure SQL offerings.
• Schema or compatibility issues can break functionality post-migration.

Best Practice:
Use tools like Azure Migrate or Microsoft’s Data Migration Assistant (DMA) to:

• Assess compatibility
• Identify unsupported features
• Recommend fixes before migration

This helps you decide whether to use Azure SQL Database, Managed Instance, or SQL on Azure VM.

2. Choosing the Wrong Azure SQL Deployment Model

Mistake:
Migrating to the wrong Azure SQL service tier. For e.g., moving a complex multi-database system to Azure SQL Database instead of Managed Instance.

Why it’s bad:

• Certain features (SQL Agent Jobs, cross-database transactions, Service Broker) aren’t supported in basic tiers.
• Can force expensive redesigns later.

Best Practice:
Choose based on workload:

Requirement	Recommended Azure Service
Single isolated DB	Azure SQL Database
Multiple DBs / Linked Servers	Azure SQL Managed Instance
Full control over OS/SQL	Azure SQL VM

3. Ignoring Network Latency and Connectivity Planning

Mistake:
Assuming Azure SQL performs the same as an on-prem connection without optimizing networking.

Why it’s bad:

• Applications can suffer from latency or timeouts.
• Security misconfiguration (like using public endpoints) increases risk.

Best Practice:

• Use Azure Private Endpoint for secure, low-latency connections.
• Place your app and database in the same region.
• Test network round-trip latency with Azure Network Watcher before go-live.

4. Not Accounting for Data Size and Migration Time

Mistake:
Underestimating how long large data transfers take or how much downtime will occur. Also Migrating outdated, redundant, or trivial (ROT) data.

Why it’s bad:

• Inflates cloud storage costs
• Slows down the migration and can negatively impact performance.
• Extended downtime affects users.
• Incomplete migration risks data inconsistency.

Best Practice:

• Incorporate a data cleansing and rationalization phase.
• Archive historical or obsolete data before the move to reduce the migration scope and save on cloud storage costs.
• Define acceptable downtime windows
• For large databases (>100 GB), use Azure Database Migration Service (DMS).
• For minimal downtime, leverage online migration like the Azure Database Migration Service (DMS), which allows continuous data synchronization until a final, short cutover.
• Test migration speed in a sandbox environment before production cutover.

5. Overlooking Index and Statistics Rebuilds After Migration

Mistake:
Assuming indexes and statistics are automatically optimized after migration.

Why it’s bad:

• Query performance may drop due to outdated statistics or fragmented indexes.
• Execution plans might not reflect Azure’s environment.

Best Practice:
Rebuild indexes and update statistics post-migration to help the optimizer generate efficient plans

ALTER INDEX ALL ON [TableName] REBUILD;
EXEC sp_updatestats;

Also, review execution plans in Azure Query Performance Insight.

6. Failing to Right-Size Resources (Over or Under-Provisioning)

Mistake:
Selecting a service tier or compute size (vCore/DTU count) in Azure that is either too small (causing performance bottlenecks) or excessively large (wasting money).

Why it’s bad:

• Over-provisioning = unnecessary cost.
• Under-provisioning = performance bottlenecks.

Best Practice:

• Run a performance baseline on your source database.
• Use DMA‘s performance data collection feature to get an accurate SKU recommendation that matches your real-world workload requirements.
• Use Azure’s performance recommendations post-migration.
• Start small → monitor → scale dynamically using Azure Auto-Scale.
• Choose vCore model for predictable scaling and flexibility.

7. Ignoring Authentication and Security Configuration

Mistake:
Continuing with SQL authentication or exposing the database via public IP. Also not defining and implementing Azure’s security measures (like virtual networks, firewalls, encryption, and proper user role-based access control) before the migration, potentially exposing sensitive data.

Why it’s bad:

• Weakens security posture.
• Increases exposure to attacks and compliance risks.

Best Practice:

• Use Azure Active Directory (AAD) authentication.
• Disable public access; use Private Link / VNet Integration.
• Enable TDE (Transparent Data Encryption) and Azure Defender for SQL.

8. Neglecting Backup, DR, and HA Planning

Mistake:
Assuming Azure “takes care of backups automatically” without configuring policies.

Why it’s bad:

• You may not meet RTO/RPO goals.
• Backups may not be stored in your desired region or retention period.

Best Practice:

• Configure Automated Backups and verify retention settings.
• For critical workloads, use Geo-Replication or Auto-Failover Groups.
• Test restore operations regularly.

9. Forgetting to Update Connection Strings and Application Settings

Mistake:
Not updating applications, APIs, or linked systems with new connection strings post-migration.

Why it’s bad:

• Applications fail to connect after cutover.
• Old credentials may expose the old server.

Best Practice:

• Store connection strings in Azure Key Vault.
• Use Managed Identity for authentication where possible.
• Validate app connectivity in staging before go-live.

10. Not Monitoring or Testing Post-Migration Performance

Mistake:
Assuming the migration is complete once the data is in Azure and failing to perform essential post-migration tasks.

Why it’s bad:

• Performance, cost, or compatibility issues may appear only under load.

Best Practice:

• Recreating server-level logins/users,
• Refreshing statistics
• Reviewing and adjusting the database compatibility level
• Use Azure Monitor and Log Analytics to track CPU, memory, DTU usage.
• Review Query Performance Insight regularly.
• Enable Automatic Tuning (where supported) for index and query plan optimization.

11. Excluding Key Stakeholders

Mistake:

Proceeding with a purely technical migration without involving business leaders, application owners, and end-users.

Why it’s bad:

It can lead to misaligned goals and post-migration validation failures.

Best Practice:

• Engage all stakeholders early.
• Application owners must test for functional integrity
• End-users should conduct User Acceptance Testing (UAT) on the migrated data to ensure continuity.

12. Lack of a Robust Backup and Recovery Plan

Mistake:

Starting the migration without a tested and verified pre-migration backup.

Why it’s bad:

Risking permanent data loss if unexpected issues arise during the process.

Best Practices:

• Always take a full, verified backup of the source database.
• Familiarize yourself with and configure Azure Backup and Azure Site Recovery for post-migration business continuity and disaster recovery.

Bonus Tip: Automate and Document Everything

Automation ensures repeatability and reliabili

Documentation should be done for the below mentioned items:

• Migration tools used
• Steps and configurations
• Rollback plan

Tools like Azure DevOps Pipelines and ARM templates help automate migrations and ensure version control.

Summary Table

#	Mistake	Consequence	Solution
1	Skipping Assessment	Compatibility issues	Use DMA before migration
2	Wrong Deployment Model	Missing features	Choose right Azure service
3	Ignoring Networking	Latency, insecurity	Use Private Endpoint
4	Underestimating Data Size	Downtime	Use DMS, online migration
5	Ignoring Indexes/Stats	Slow queries	Rebuild & update stats
6	Wrong Resource Sizing	Cost/performance issues	Monitor and auto-scale
7	Weak Security Setup	Data risk	Use AAD & encryption
8	No Backup Strategy	Data loss	Set retention & geo-replication
9	Outdated Connections	App failure	Update strings & identities
10	No Post-Migration Check	Hidden issues	Monitor & tune continuously
11.	Excluding Key Stakeholders	Misaligned goals and post-migration validation failures	Engage all stakeholders early. Functional Integrity Testing and User Acceptance Testing (UAT) must be done
12.	Lack of a Robust Backup and Recovery Plan	Risk of permanent data loss if unexpected issues arise midway	Always take a full, verified backup of source database before starting process

Conclusion

Migrating to Azure SQL isn’t just about moving data. It’s about modernizing your database ecosystem for performance, security, and scalability.

By avoiding these 12 mistakes, you’ll ensure your Azure migration is smooth, cost-effective, and future-ready.

• Plan carefully.
• Test thoroughly.
• Monitor continuously.

Your migration to Azure should feel like an upgrade not a firefight.

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