Migrating data workloads to the Azure public cloud is a comprehensive process that involves several critical steps to ensure a smooth transition. This blog breaks down the migration process into four main parts: assessing workload readiness, exploring migration options, comparing these options, and planning the cutover phases.  

Assessing Workload Readiness

Before migrating any workload to Azure, it’s crucial to evaluate its readiness. Here are some key steps:

Technical Compatibility Assessment

• Azure Migrate: Use Azure Migrate to assess your workloads. It provides insights into compatibility, dependencies, and sizing expectations.
• Operating System Support: Ensure that your workload’s operating system is supported in Azure.
• Server Size: Evaluate whether the current server size aligns with Azure VM sizes.
• Data Change Rates: Consider data change rates that might affect replication.

Dependencies Assessment

• Cross-Datacenter Dependencies: If you’re migrating workloads from multiple datacenters, assess dependencies between them.
  • Visualize dependencies using Azure Migrate and Modernize.
  • Group dependencies to identify IP addresses and ports required to support the workload.
  • Evaluate downstream dependencies and bidirectional dependencies.

Options for Azure Data Workload Migration

When it comes to migrating data workloads to Azure, you have several options:

Azure Database Migration Service (DMS)

• Purpose: Migrate on-premises databases to Azure.
• Supported Databases: SQL Server, MySQL, PostgreSQL, Oracle, MongoDB, etc.
• Features:
  • Online and offline migrations
  • Schema and data migration
  • Minimal downtime
  • Continuous data replication
  • Assessment reports

Azure Site Recovery (ASR)

• Purpose: Replicate virtual machines (VMs) from on-premises to Azure.
• Supported Workloads: VMs running Windows or Linux.
• Features:
  • Replication and failover
  • Application-consistent snapshots
  • Cross-region failover
  • Integration with Azure Backup

Azure Data Factory

• Purpose: ETL (Extract, Transform, Load) and data integration.
• Features:
  • Data movement and transformation
  • Orchestration of data workflows
  • Integration with various data sources (on-premises, cloud, SaaS)

Azure Data Box

• Purpose: Offline data transfer for large volumes.
• Features:
  • Shipped physical devices (Data Box, Data Box Disk, Data Box Heavy)
  • Secure data transfer to Azure data centers

Comparing Options

Evaluate the options based on factors such as workload type, data volume, downtime tolerance, and cost. Consider the following questions:

• Is your workload a database, VM, or data pipeline?
• How much data needs to be migrated?
• Can you tolerate downtime during migration?
• What’s your budget?

Cutover Phases

Plan the cutover phases carefully:

• Assessment Phase: Evaluate readiness and choose the migration option.
• Pilot Migration: Test the migration process with a subset of data.
• Full Migration: Migrate the entire workload.
• Post-Migration Validation: Verify data integrity and functionality.

Conclusion

By following these guidelines, businesses can effectively evaluate technical compatibility, manage dependencies, and select the appropriate tools and strategies for a successful migration. This structured approach ensures minimal downtime, maintains data integrity, and optimizes the benefits of moving to the Azure cloud.

Are you planning to move to the Azure Cloud?‍