What changed
The store never stopped selling during the move from Shopify Plus to Sylius Plus, and traffic shifted over only after each stage passed its checks.
Case studies
eCommerce
Phased replatforming without downtime
Client:NAF NAF
Program:Shopify Plus → Sylius Plus
Proof in production
What we built
A phased rollout plan with both platforms running in parallel, and validation gates at every step.
What's protected
The migration moved in stages, with a rollback option at each one.
NAF NAF Spain migrated from Shopify Plus to Sylius Plus while the store remained live.
The approach prioritized staged cutovers, validation gates, and explicit ownership for the highest risk areas first. The goal was controlled change on a revenue system with predictable behavior during the transition.
01
Context and constraints
The store operated with live traffic and revenue sensitive flows.
The migration path had to protect checkout continuity, SEO behavior, and operational correctness across integrations and data flows.
Constraints that shaped decisions
Live revenue and daily operations during migration
Checkout and payments cannot be paused
SEO exposure during URL and rendering changes
Integrations with systems of record
Limited rollback options after data moves
02
Failure modes prioritized
Scope was defined around failure modes that can silently damage revenue or operations. The plan assumed issues would surface under real traffic, so detection and gating were built into each stage.
Primary failure modes
•Checkout regressions in edge flows
•Data correctness drift between systems
•SEO regressions from routing and rendering differences
•Integration coupling with ERP, CRM, PIM
•Observability gaps during traffic cutovers
03
Approach: staged cutovers from Shopify Plus to Sylius Plus
The migration was split into stages to limit blast radius and keep rollback realistic at each step. Each stage had entry criteria, validation gates, and exit criteria before exposure expanded.
Stage pattern used
01Identify high risk domains: checkout, catalog, pricing, promotions, integrations
02Establish data contracts and ownership for systems of record
03Build parallel paths where traffic can be segmented
04Cut over in stages, expanding exposure only after validation
05Validate data correctness and revenue-facing flows after each cutover window
04
Data migration and correctness controls
Moving platforms changes data shape, lifecycle, and failure behavior. Correctness controls were designed to detect drift early and limit rework during live cutovers.
Controls used during migration
Consistency checks across product, price, inventory, orders
Reconciliation routines for the most revenue sensitive entities
Idempotent sync patterns for integration flows
Clear ownership for data contracts and exceptions handling
05
SEO continuity under platform change
SEO risk increases when rendering, routing, and URL structure shift.
Controls focused on preserving indexing behavior and detecting regressions during staged exposure.
What was validated
Redirect coverage and indexing signals for preferred URLs
Crawl behavior and indexing signals during transitions
Key template parity for critical landing paths
Monitoring for regressions during each cutover stage
06
Ownership lines
Ownership was made explicit across integrations, data flows, release responsibility, and incident response. That reduced hidden dependencies and ambiguity during cutovers.
Boundary examples
- Systems of record ownership and data contracts
- Integration responsibilities and failure handling
- Release and rollback responsibility per stage
- Monitoring ownership and incident response process
07
Outcome in operational terms
The store stayed operational during migration stages while risk concentrated areas moved behind validation gates.
Post cutover behavior remained predictable under real traffic, with clearer boundaries for safe change.
What changed after staged cutovers
Smaller blast radius for releases
More explicit validation routine for high risk flows
Fewer unknown dependencies in integrations
Higher confidence in making changes safely
What to take from this case
Phased migration works when scope is defined by failure modes and ownership lines. The same pattern applies when change cost and release risk reached a point where incremental fixes stop reducing exposure.
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