Migration Validation

Data integrity validation confirms that all data migrated correctly from source to target without corruption or loss. This is the most critical post-migration activity, as data issues discovered after cutover can have severe business impact.

Row Count Validation is the simplest integrity check. Compare the number of rows in each source table against the corresponding target table. While simple, this check catches most bulk data loss issues. Automate row count comparisons using SQL queries against both source and target databases. For example, if your source Orders table has 1,247,893 rows, the target must match exactly.

Checksum Validation provides deeper integrity verification. Calculate MD5 or SHA-256 checksums for entire tables or specific columns at the source, then compare against target values. This catches subtle data corruption that row counts would miss. DMS includes built-in validation that can compare checksums automatically. For large tables, partition checksums by date range or primary key to reduce validation time.

Data Type Mapping Validation ensures that source data types translated correctly to target types. Common issues include datetime precision loss, numeric overflow, character encoding problems, and NULL handling differences. For example, Oracle NUMBER(19,4) to PostgreSQL NUMERIC might have subtle rounding differences. Test with edge cases: maximum values, minimum values, NULLs, and special characters.

Business Logic Validation goes beyond technical checks to verify that data makes business sense. Run key business queries against both source and target and compare results. For a financial system, validate that account balances reconcile, transaction totals match, and reporting queries produce identical results. Create automated validation suites that run critical business queries and alert on discrepancies.

Automated Validation Frameworks reduce manual effort and catch issues early. Build scripts that execute validation checks in parallel, generate comparison reports, and flag anomalies. Tools like AWS Glue can run validation ETL jobs. Custom Lambda functions can trigger validation when DMS tasks complete. Store validation results in a tracking database for audit purposes and trend analysis.

Reconciliation Reports provide ongoing assurance that source and target remain synchronized during CDC phases. Monitor DMS task metrics for latency, throughput, and error rates. Track pending changes to ensure CDC is keeping up with source database activity. Set alerts for validation failures or CDC lag exceeding acceptable thresholds.

Cutover is the critical period when you switch production traffic from the old environment to the new cloud environment. A well-planned cutover minimizes downtime and ensures a smooth transition.

Cutover Planning begins weeks before the actual event. Define the cutover window, identify all tasks, assign owners, and establish success criteria. The cutover plan typically includes: stopping application writes to the source, performing final data synchronization, validating data integrity, updating DNS or load balancer configurations, starting application services in the cloud, and validating functionality.

Final Synchronization depends on your migration approach. For DMS migrations, stop CDC, verify no pending changes, and run final validation. For MGN migrations, initiate cutover which performs a final replication sync. Document the expected duration of each step. A typical database migration might require 10-15 minutes for final sync plus 5-10 minutes for validation.

DNS and Traffic Switching routes users to the new environment. Options include updating DNS records with low TTL, modifying load balancer targets, or using weighted routing to gradually shift traffic. For example, route 10% of traffic to the new environment, monitor for issues, then increase to 50% and finally 100%.

Rollback Procedures are essential safety nets. Before cutover, define clear rollback criteria: what conditions trigger rollback, who has authority to initiate it, and how rollback is executed. Rollback typically involves reverting DNS changes, re-enabling connections to the old environment, and syncing any data changes that occurred during the cutover window.

Hypercare is the intensive support period immediately following cutover, typically lasting 1-2 weeks. During hypercare, engineering teams monitor all systems closely, respond rapidly to issues, and make quick fixes. Establish a war room or virtual bridge for real-time communication. Monitor application performance metrics, error rates, user-reported issues, and infrastructure health.

Post-Migration Monitoring should focus on performance baselines, error patterns, and resource utilization. Compare cloud environment performance against pre-migration baselines. Watch for common post-migration issues: connection pool exhaustion, DNS resolution problems, certificate mismatches, and permission errors. Document all issues and their resolutions for future migrations.