Skip to content
Deploint

Case study 03 / Technology

Enterprise Cloud Modernization

An incremental path from a monolithic, data-center-hosted platform to containerized services on a governed multi-account cloud landing zone.

Concept Architecture

Illustrative reference architecture, not a client engagement. Outcomes described here are design goals, not measured results.

Talk to an EngineerExplore the architecture

Reference flow

05 stages

Reference flow for Enterprise Cloud Modernization: Monolith, then Strangler APIs, then Services, then Kubernetes, then Observability.

01Challenge

Why this problem is hard.

A revenue-critical monolith runs in a company-owned data center. It still works, but releases are infrequent and risky, scaling means buying hardware and only a few engineers understand the deployment process. A full rewrite would pause feature delivery for too long and carries well-known delivery risk.

  • 01

    Coupled releases

    Every change ships with every other change. A defect in one module blocks the whole release train.

  • 02

    Shared database

    Modules communicate through a single schema, so extracting anything risks breaking something else.

  • 03

    Hand-built environments

    Servers are configured by hand, environments drift and recovery depends on individual knowledge.

  • 04

    Unattributed cost

    Infrastructure cost is not attributed to products or teams, which makes trade-offs hard to discuss.

02Business context

Assumptions and constraints.

The concept assumes a product organization with several engineering squads, a mature but poorly modularized codebase, a relational database at its center and a decision to adopt a public cloud provider. Feature delivery has to continue throughout.

Constraints the design must respect

01No feature freeze
Modernization runs alongside product work. Every step must be releasable on its own.
02Zero-downtime cutovers
Traffic moves gradually, with a tested rollback at every step.
03Governance from day one
Accounts, networks, identity and guardrails are defined in code before workloads arrive.
04Explicit data ownership
During transition the monolith and new services share data. Each table has exactly one writer at a time.

03Architecture

Reference architecture.

Six stages that move capability out of the monolith one reversible slice at a time, onto a platform that was governed before the first workload arrived. Select a stage to see what it does and the components involved.

Map domains, call paths, data ownership and change frequency to find seams. Areas with high change and low coupling are extracted first.

  • Domain mapping
  • Dependency analysis
  • Change-frequency heatmap
  • Seam identification

Key patterns

  • Strangler fig facade
  • Change data capture
  • Transactional outbox
  • Landing zone as code

04Technology

Representative technology.

Technologies we would consider for this concept. Final selection follows the organization's existing standards, skills and estate. Listing a technology does not imply a vendor relationship.

  • 01

    04 items

    Platform

    • Managed Kubernetes
    • Terraform
    • GitOps controller
    • Container registry with scanning

  • 02

    04 items

    Integration

    • API gateway
    • Change data capture
    • Event streaming
    • Transactional outbox

  • 03

    04 items

    Delivery

    • CI/CD pipelines
    • Feature flags
    • Contract testing
    • Progressive delivery

  • 04

    04 items

    Operations

    • OpenTelemetry
    • SLO-based alerting
    • Centralized logging
    • Cost allocation

05Engineering approach

Principles behind the design.

The decisions that shape every stage of the architecture, and why we would hold to them under delivery pressure.
  1. 01

    Incremental, reversible steps

    Every move, from a route to a table, has a rollback. Irreversible steps are scheduled deliberately and rehearsed.

  2. 02

    Platform before services

    Teams extract services onto a paved road instead of inventing deployment and operations for each one.

  3. 03

    Data ownership drives sequencing

    A service is extracted only when its data can be owned by it. Shared tables, not code, are the real constraint.

  4. 04

    Measure before and after

    Latency, error rates and cost are compared per route at each cutover, using the same telemetry.

06Implementation

A phased delivery path.

Each phase ends with a working, reviewable increment and an explicit decision on whether and how to continue.

Delivery path

04 phases

Delivery phases: Assess & plan, then Foundation, then Extract & migrate, then Rehost the remainder
  1. 01Assess & plan
  2. 02Foundation
  3. 03Extract & migrate
  4. 04Rehost the remainder
  1. Phase 0101

    Assess & plan

    Domain and data mapping, a target architecture and a sequenced backlog of extractions.

    Deliverables

    • Domain map
    • Target architecture
    • Extraction backlog
    • Risk register
  2. Phase 0202

    Foundation

    Landing zone, CI/CD, observability and the strangler facade in front of the monolith, with no behavior change.

    Deliverables

    • Landing zone in code
    • Pipelines and templates
    • Facade in production
    • Baseline SLOs
  3. Phase 0303

    Extract & migrate

    Move domains one at a time: new service, CDC sync, gradual traffic shift, then retire the legacy path.

    Deliverables

    • Extracted services
    • CDC pipelines
    • Cutover runbooks
    • Retired legacy code
  4. Phase 0404

    Rehost the remainder

    Containerize what remains of the monolith on the platform, then decide case by case whether further extraction is worth it.

    Deliverables

    • Containerized monolith
    • Data center exit plan
    • Ownership model
    • Cost reporting

07Intended outcomes

Design goals, not results.

What this architecture is designed to achieve. These are intended outcomes for a concept, not measured results from a client engagement.

  • Design goal 01Intended

    Independent releases

    Teams ship their services on their own schedule rather than waiting for a shared release train.

  • Design goal 02Intended

    Reproducible environments

    Infrastructure defined in code can be rebuilt, reviewed and audited like any other change.

  • Design goal 03Intended

    Smaller blast radius

    Gradual traffic shifting and rehearsed rollbacks limit the impact of any single step.

  • Design goal 04Intended

    Visible cost and ownership

    Every workload is attributable to a team and product, so cost trade-offs can be discussed explicitly.

Concept Architecture

No figures are attached to these goals. Actual results depend on the environment, data and delivery, and would be measured against baselines agreed at the start of a real engagement.

08Lessons & risks

Engineering lessons and risks to manage.

Where designs like this tend to run into trouble, and what we would plan for from the start.

  • Risk 01

    The database is the hard part

    Code extraction is rarely the bottleneck. Untangling shared tables and reporting queries usually is.

  • Risk 02

    Dual running costs money

    Operating the data center and the cloud in parallel is expensive. Plan the exit sequence early.

  • Risk 03

    Distribution adds failure modes

    Network calls fail in ways in-process calls do not. Timeouts, retries and idempotency must be designed, not discovered.

  • Risk 04

    Not everything should be a service

    Some modules are better left in a well-run modular monolith. Extraction should follow business value.

Start an engineering conversation

Facing a similar engineering problem?

Bring us the problem and the constraints around it. We'll help architect the system.

Talk to an EngineerView Case Studies