Cloud Migration
Cloud database migration — zero data loss, minimal downtime.
Moving a production database to the cloud is not a copy-paste operation. Data loss, downtime, and post-migration performance degradation are common — and entirely avoidable with the right DBA guiding the process.
What can go wrong
Cloud migrations fail more often than you'd expect.
Database cloud migration is the most data-sensitive operation in your infrastructure. The risks aren't hypothetical — they're what we see when clients come to us after a failed DIY migration.
Silent data loss
Character encoding differences, timezone handling, and type coercions between source and target engines cause data to silently corrupt or truncate — often not discovered until weeks after cutover.
Extended downtime windows
Underestimating data volume or replication lag leads to cutover windows that stretch from hours to days. Applications go down, revenue stops, customers notice.
Post-migration performance regression
Cloud-managed databases behave differently from on-premises instances — execution plans change, auto-tuning features interfere, and resource throttling creates new bottlenecks that didn't exist before.
Missed dependencies
Linked servers, agent jobs, login mappings, certificates, and cross-database queries are routinely missed in migration planning — discovered at cutover when nothing works.
Rollback without a plan
Most teams have a go-forward plan but no tested rollback. When the cutover hits an unexpected problem at 2am, there's no clean path back to the source system.
Security misconfiguration
Cloud IAM models differ significantly from on-premises SQL logins. Permissions that worked locally fail in the cloud, or worse — are over-permissioned to get things working quickly.
Our process
How we run a cloud database migration.
Every migration follows the same structured five-phase process. Nothing is improvised at cutover. The plan is written, tested, and reviewed before we touch production.
- 01
Discovery & Dependency Mapping
We document every object that needs to migrate — schemas, stored procedures, agent jobs, logins, linked servers, certificates, CLR assemblies, and cross-database dependencies. Nothing gets discovered at cutover.
- 02
Target Environment Sizing & Configuration
We size the target cloud instance based on your actual workload profile — not the vendor's default recommendation. Memory, IOPS, network throughput, and connection limits are set correctly before any data moves.
- 03
Data Replication & Validation
Initial load via backup restore, DMS, or native replication — then continuous sync to minimize cutover lag. Row counts, checksum validation, and spot-check queries run against both systems throughout. We don't guess at data integrity.
- 04
Cutover Planning & Dry Run
The cutover runbook is written step-by-step with timing estimates, go/no-go criteria, and explicit rollback steps for each phase. We run a full dry run in a staging environment before touching production.
- 05
Post-Migration Validation & Tuning
After cutover, we monitor performance for the first 48–72 hours, compare execution plans against the source baseline, and address any cloud-specific tuning required. The engagement ends when performance is confirmed stable.
Scope of work
What's included in a migration engagement.
Migration planning & scope documentation
Full inventory of objects, dependencies, and data volumes. Written migration plan with timeline, resource requirements, and risk register before any execution begins.
Schema & data migration
Source schema conversion (handling dialect differences for cross-platform migrations), bulk data load, and continuous incremental sync to minimize the cutover window.
Security & permissions migration
User accounts, roles, and permissions migrated and mapped to cloud IAM equivalents. Principle of least privilege enforced — no over-permissioning to make things work quickly.
Post-migration performance tuning
Cloud execution plans frequently differ from on-premises baselines. We baseline performance before cutover and tune the cloud environment to match or exceed it after.
Monitoring setup
CloudWatch, Azure Monitor, or equivalent configured with appropriate thresholds and alerting for your workload — before you go live, not after the first incident.
Documentation & handoff
Post-migration documentation covering the new environment architecture, configuration decisions made, known differences from the source system, and runbook for ongoing operations.
Where we migrate to
Source and target platforms we support.
We handle same-engine migrations (SQL Server on-prem → AWS RDS SQL Server) and cross-engine conversions (SQL Server → PostgreSQL, Oracle → Aurora PostgreSQL). Each has different complexity and risk profile, and we scope accordingly.