AWS Data Migration Strategies for Data Engineers

Migration Strategies Overview

Data migration on AWS involves transferring data from on-premises databases, other cloud providers, or existing AWS services to new target environments. Successful migrations require careful planning, the right tools, and robust validation processes.

Key Migration Approaches

AWS provides several migration strategies depending on your source systems, downtime tolerance, and data volume:

1. Rehost (Lift and Shift) - Move applications and data as-is to AWS infrastructure
2. Replatform (Lift and Reshape) - Make cloud optimizations while migrating
3. Refactor/Re-architect - Redesign applications to leverage cloud-native services
4. Repurchase - Move to different software products (e.g., SaaS)
5. Retain - Keep certain applications on-premises

AWS Migration Tools Ecosystem

AWS offers a comprehensive suite of migration tools:

• AWS Database Migration Service (DMS) - Continuous data replication with high availability
• AWS Schema Conversion Tool (SCT) - Automated schema conversion between database engines
• AWS DataSync - High-speed data transfer for file-based storage
• AWS Snow Family - Physical devices for large-scale offline data transfer
• AWS Migration Hub - Central tracking for migration progress

Online vs Offline Migration

Understanding the distinction between online and offline migration is crucial for planning successful data movement strategies.

Online Migration

Online migration allows data to be transferred while source systems remain operational. This approach minimizes downtime and is ideal for:

• Business-critical applications that cannot tolerate extended outages
• Large databases where downtime would impact revenue
• Continuous synchronization requirements between source and target

Key Online Migration Services:

• AWS Database Migration Service (DMS)
• AWS Schema Conversion Tool (SCT) with continuous replication
• Change Data Capture (CDC) mechanisms

Offline Migration

Offline migration involves taking the source system offline during data transfer. This approach is suitable for:

• Non-critical systems with flexible maintenance windows
• Small to medium datasets that can be transferred quickly
• One-time migrations where continuous sync isn't required

Key Offline Migration Services:

• AWS DataSync for file-based transfers
• AWS Snow Family (Snowcone, Snowball, Snowmobile) for large datasets
• AWS Transfer Family for SFTP-based transfers

Migration Decision Matrix

When choosing between online and offline migration, consider these factors:

DMS Migration Patterns

AWS Database Migration Service (DMS) supports several migration patterns depending on your requirements:

1. One-Time Migration

A single data movement from source to target with no ongoing synchronization.

Use Cases:

• Development environment setup
• Historical data migration
• One-time data consolidation

2. Continuous Replication

Ongoing synchronization from source to target using Change Data Capture (CDC).

Use Cases:

• Production database migrations
• Cross-region replication
• Disaster recovery setups

3. Bidirectional Replication

Two-way synchronization between source and target databases.

Use Cases:

• Active-active database configurations
• Multi-region deployments
• Hybrid cloud scenarios

DMS Task Configuration Best Practices

When configuring DMS tasks, follow these best practices:

1. Endpoint Configuration

   • Use SSL connections for data security
   • Configure proper character set mapping
   • Set appropriate batch apply settings

2. Replication Instance Sizing

   • Choose instance type based on data volume
   • Enable Multi-AZ for high availability
   • Monitor CPU and memory utilization

3. Table Mapping

   • Include only necessary tables
   • Configure proper primary key selection
   • Use filter rules for selective migration

4. Error Handling

   • Configure error logging
   • Set up CloudWatch alarms
   • Implement dead letter queue for failed records

Migration Validation

Migration validation ensures data integrity and completeness after migration. A robust validation framework is essential for successful migrations.

Validation Components

1. Row Count Comparison

   • Compare source and target row counts
   • Identify missing or extra records
   • Track record counts over time

2. Data Checksum Validation

   • Calculate checksums for source and target data
   • Compare checksums to identify data corruption
   • Validate specific columns or entire tables

3. Schema Validation

   • Compare table structures
   • Validate data types and constraints
   • Check index and key configurations

4. Business Logic Validation

   • Validate calculated fields
   • Test stored procedures and functions
   • Verify data relationships

Automated Validation Scripts

Create automated validation scripts to ensure consistent and repeatable validation:

-- Row Count Validation
SELECT 
    'source' as location,
    COUNT(*) as row_count,
    MAX(last_updated) as max_timestamp
FROM source_table
UNION ALL
SELECT 
    'target' as location,
    COUNT(*) as row_count,
    MAX(last_updated) as max_timestamp
FROM target_table;

-- Checksum Validation
SELECT 
    CHECKSUM_AGG(CHECKSUM(*)) as source_checksum
FROM source_table
UNION ALL
SELECT 
    CHECKSUM_AGG(CHECKSUM(*)) as target_checksum
FROM target_table;

Validation Metrics to Track

Monitor these key metrics during validation:

1. Data Completeness - Percentage of records successfully migrated
2. Data Accuracy - Percentage of records matching source data
3. Validation Coverage - Percentage of tables/columns validated
4. Error Rate - Number of validation failures per table
5. Performance Impact - Validation overhead on source/target systems

Migration Planning Checklist

A comprehensive migration plan should include:

Pre-Migration Phase

• Assess source database compatibility
• Identify migration strategy (online/offline)
• Estimate data volume and migration time
• Plan validation approach
• Set up monitoring and alerting
• Create rollback procedures

Migration Execution Phase

• Configure DMS replication instance
• Set up source and target endpoints
• Create and test DMS tasks
• Monitor migration progress
• Handle errors and exceptions
• Validate data during migration

Post-Migration Phase

• Perform comprehensive data validation
• Test application functionality
• Update connection strings
• Monitor performance metrics
• Decommission old systems
• Document lessons learned

Common Migration Pitfalls

Avoid these common mistakes during database migrations:

1. Insufficient Testing - Always test migrations in non-production environments first
2. Ignoring Performance - Monitor source and target performance during migration
3. Missing Validation - Implement comprehensive validation before cutover
4. Poor Communication - Keep stakeholders informed of migration progress and issues
5. No Rollback Plan - Always have a rollback strategy in case of failure

Performance Optimization Tips

Optimize migration performance with these techniques:

1. Parallel Processing - Use multiple DMS tasks for different table groups
2. Resource Sizing - Choose appropriate replication instance types
3. Network Optimization - Use dedicated network connections when possible
4. Batch Processing - Configure appropriate batch sizes for data transfer
5. Index Management - Consider dropping indexes during migration and rebuilding afterward

Architecture Flow

Interview Q&A

General Migration Questions

Q: What is the difference between AWS DMS and AWS SCT?

A: AWS DMS (Database Migration Service) handles the actual data movement between databases, while AWS SCT (Schema Conversion Tool) converts database schemas from one engine to another. DMS is used for data transfer, whereas SCT is used for schema and code conversion when migrating between different database engines (e.g., Oracle to PostgreSQL).

Q: When would you choose online migration over offline migration?

A: Choose online migration when:

• The source database cannot tolerate extended downtime
• You need continuous data synchronization during migration
• The data volume is large and would take too long to transfer offline
• Business operations must continue uninterrupted during migration

Q: How do you handle schema changes during a DMS migration?

A: Schema changes during DMS migration can be handled by:

1. Pausing the DMS task
2. Applying the schema change to both source and target
3. Resuming the DMS task
4. Using DMS schema conversion for engine-specific changes
5. Implementing DDL replication if supported by the source database

Technical Questions

Q: Explain the difference between full load and CDC in DMS.

A: Full load copies all existing data from source to target in a single operation. CDC (Change Data Capture) captures and replicates ongoing changes (inserts, updates, deletes) after the full load completes. Typically, DMS tasks run full load first, then transition to CDC for continuous synchronization.

Q: How do you validate data integrity after migration?

A: Data integrity validation includes:

1. Row count comparison between source and target
2. Checksum validation for data accuracy
3. Sampling-based validation for large datasets
4. Business logic validation for calculated fields
5. Schema validation for structure integrity
6. Performance testing to ensure query optimization

Q: What are the limitations of AWS DMS?

A: Key limitations include:

• Primary key must exist for CDC on tables
• Some data types may not be fully supported
• Performance can be affected by network latency
• Complex stored procedures require manual conversion
• Some database-specific features may not transfer

Architecture Questions

Q: Design a migration architecture for a 10TB Oracle database to Aurora PostgreSQL.

A: Architecture would include:

1. AWS SCT for schema conversion from Oracle to PostgreSQL
2. AWS DMS with large replication instance for data transfer
3. Full load followed by CDC for continuous synchronization
4. Multi-AZ DMS deployment for high availability
5. CloudWatch monitoring for performance tracking
6. Custom validation scripts for data integrity
7. Blue/green deployment for cutover with minimal downtime

Q: How would you handle a cross-region migration with high latency?

A: For cross-region migrations:

1. Use AWS Snow Family for initial bulk data transfer
2. Set up DMS with compression enabled
3. Configure DMS to use dedicated network connections
4. Implement batch processing to reduce round trips
5. Use error handling and retry logic for network issues
6. Monitor replication lag and adjust batch sizes accordingly

Scenario-Based Questions

Q: Your DMS task is experiencing high replication lag. How do you troubleshoot?

A: Troubleshooting steps:

1. Check replication instance CPU and memory utilization
2. Verify network bandwidth between source and target
3. Review DMS task settings (batch apply, parallel load)
4. Check for long-running transactions on source
5. Verify target database performance
6. Consider upgrading replication instance type
7. Optimize table mappings to reduce processing overhead

Q: A client wants to migrate 500GB of data with zero downtime. What approach do you recommend?

A: Recommended approach:

1. Use AWS DMS with CDC (Change Data Capture)
2. Set up initial full load during off-peak hours
3. Enable CDC for continuous synchronization
4. Monitor replication lag to ensure near-zero latency
5. Perform comprehensive validation before cutover
6. Use blue/green deployment for application cutover
7. Have rollback plan ready in case of issues

Q: How do you handle data type mismatches between source and target databases?

A: Handling data type mismatches:

1. Use AWS SCT to identify conversion requirements
2. Configure DMS data type mappings in task settings
3. Use transformation rules for custom conversions
4. Test with sample data before full migration
5. Document all type conversions for reference
6. Validate converted data accuracy post-migration

Best Practices Questions

Q: What monitoring should be in place during a database migration?

A: Essential monitoring includes:

1. DMS task metrics (CDCLatencySource, CDCLatencyTarget)
2. Replication instance performance (CPU, memory, storage)
3. Network throughput and latency
4. Source and target database performance
5. Error rates and failed transactions
6. CloudWatch alarms for critical thresholds
7. Custom dashboards for migration progress

Q: How do you ensure zero data loss during migration?

A: Zero data loss strategies:

1. Use DMS with CDC for continuous synchronization
2. Monitor replication lag and alert on increases
3. Implement comprehensive validation scripts
4. Perform row-level validation for critical tables
5. Use checksums for data integrity verification
6. Maintain source database until validation complete
7. Have rollback procedures ready

Q: What is the recommended approach for migrating a highly transactional database?

A: For highly transactional databases:

1. Use online migration with CDC
2. Minimize full load window during low activity
3. Configure DMS for minimal impact on source
4. Monitor source database performance continuously
5. Use parallel processing for large tables
6. Implement error handling for lock contention
7. Plan cutover during minimal activity window

Q: How do you handle encoding and character set issues during migration?

A: Character set handling:

1. Identify source database character set
2. Configure DMS endpoint with correct encoding
3. Use character set mapping in DMS task settings
4. Test with international characters
5. Validate special characters post-migration
6. Document encoding conversions
7. Monitor for character corruption during migration

Q: Explain the concept of a blue/green deployment for database migration.

A: Blue/green deployment for migration:

1. Blue Environment: Current production database
2. Green Environment: New migrated database
3. Synchronization: Keep green in sync with blue using CDC
4. Testing: Validate green environment thoroughly
5. Cutover: Switch application traffic from blue to green
6. Rollback: Quickly switch back to blue if issues occur
7. Decommission: Remove blue environment after stability

This approach provides a safe migration path with minimal risk and the ability to rollback quickly if needed.

Summary

This topic covered the key concepts of AWS data engineering. Review the architecture diagrams, practice the interview questions, and understand the trade-offs between different service options.

Next Steps

Continue to the next topic to build on your AWS data engineering knowledge.

Knowledge Check

See Also

• AWS Data Migration
• AWS DMS
• AWS Glue