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Zero Downtime Platform Migration

Migrating 300+ enterprise applications from on-premises Kubernetes to Azure without a single customer facing incident
Published: Feb 2024, Updated with Claude: Oct 2025
Timeline: 6 weeks
Team: Platform + SRE
Scope: 300+ apps

Migration Architecture

On-Premises Infrastructure

Self-managed Kubernetes cluster

SQL Server
Primary DB
K8s Cluster
300+ apps
Monitoring
Legacy stack
Limited scalability, single point of failure, painful to maintain

Azure Cloud Platform

Managed, scalable, resilient

Azure SQL
Managed DB
AKS
300+ apps
Azure Monitor
Full observability
Autoscaling
Dynamic capacity
High availability, automated scaling, zero downtime delivered

Technology Stack Evolution

Week 1-3: Failed Approach

SQL Server Replication āŒ Failed

Unpredictable lag (500ms to 8 seconds), rollback failures

Result: 90-minute outage in test environment, manual rollback, migration halted

Week 3-6: Pivot to Success

Liquibase āœ… Success

Version controlled schema, easy rollback

Azure DMS āœ… Success

Optimized bulk transfer, handles edge cases

CDC (Change Data Capture) āœ… Success

Sub-second sync, log-based, resilient

Result: Zero customer-facing incidents, 300+ apps migrated flawlessly

Progressive Traffic Shifting Strategy

Initial
0%
0 days
Baseline
Canary
1%
3 days
Stable
Early Adopters
10%
5 days
Passing
Early Majority
50%
7 days
Validated
Full Cutover
100%
Immediate
Complete

Hourly Data Validation Monitoring

Real-time validation during CDC sync phase. Caught drift before it hit production.

Row Count Validation
100%
Perfect match throughout migration
Checksum Validation
100%
Zero data corruption detected
Avg Sync Lag
0.18s
Sub-second CDC replication

What We Actually Learned

One Tool Can't Do Everything

SQL Server replication was supposed to be our entire migration solution. It wasn't good at any single job. The moment we split the work into schema versioning, bulk transfer, and continuous sync, everything became simpler and more reliable.

Validation Catches Everything

Hourly row count checks and checksum validation found problems we would have completely missed. Not because we're bad at testing, but because real production data is weird. Edge cases exist. Validation caught them before they mattered.

Feature Flags Are Essential

Progressive traffic shifting with feature flags meant we could ship confidently. If 1% broke, only a few requests were affected. By the time we hit 100%, we'd validated against massive scale with zero risk.

Test Failures Save Production Incidents

Week 3's failure was painful internally but invaluable. We caught it before production. We learned exactly what was wrong. We fixed it. Then we executed flawlessly because we'd already failed once in a safe place.

Observability First, Always

We deployed monitoring before we deployed apps. When something went wrong during traffic shifting, we saw it in seconds, not hours. The observability infrastructure was the thing that let us move fast safely.

Key Takeaways

Zero Downtime Migration Isn't About Perfection

It's about building escape routes before you need them. Feature flags, observability, validation, rollback procedures. If any one of those failed, we would have had an incident. Having all of them meant we could move confidently.

Failing in Testing Prevents Failing in Production

Week 3's crisis was the best thing that could have happened. We failed safely. We learned exactly what was wrong. We fixed it. Then we executed flawlessly because we'd already lived through the failure once.

Progressive Delivery is More Important Than Speed

We could have forced all 300 apps over in one big bang. We would have crashed. Instead, we moved slowly. 1%, then 10%, then 50%, then 100%. Each stage validated everything at that scale. By the time we hit 100%, we'd proven it worked 4 different ways.

The Right Tools Matter

Liquibase, DMS, and CDC weren't fancy or novel. But each one was designed for the specific job it had to do. Using them meant we weren't fighting the tools. We were using them correctly.

Observability Enables Confidence

We deployed monitoring before we deployed applications. This is backwards from how most teams work. But it's what let us move fast. We could see problems in seconds. We could make decisions with real data instead of guesses.

For Your Team

If You're Planning a Migration

Don't assume one approach will work. Test your strategy on non critical workloads first. Let it fail. Learn from the failure. Then execute on critical workloads. The time you spend failing safely will save you catastrophic failures in production.

If You're Running the Migration

Progressive delivery isn't conservative. It's confident. You're proving each stage works at real scale before moving to the next. Feature flags and observability are your safety net. Use them.

If You're Supporting the Migration

Operations needs visibility. Deploy monitoring first. Every critical path needs dashboards. Every service needs alerts. The moment something deviates from baseline, you need to know. This infrastructure is what makes fast movements safe.

Zero downtime migrations are possible. But only if you're willing to move slowly, observe constantly, and have good fallback plans. Teams that do this don't just succeed. They do it without drama.