Catalog
concept#Platform#Architecture#DevOps

Containerization

Packages applications and dependencies into portable containers to ensure consistency across environments and improve deployment speed and resource utilization.

Containerization packages applications and their runtime dependencies into isolated, portable containers based on standardized images and runtimes.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Advanced

Technical context

Container registry (e.g., Harbor, Docker Hub)Orchestrator (e.g., Kubernetes)CI/CD toolchain (e.g., GitLab CI, GitHub Actions)

Principles & goals

Isolation of runtime dependenciesImmutability of imagesAutomated build and trust chains
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Insecure base images or unmanaged dependencies
  • Misconfigured orchestration can cause availability issues
  • Image bloat and inconsistent build pipelines
  • Minimize image sizes using multi-stage builds and cleaned dependencies.
  • Sign and scan images in the pipeline before publishing.
  • Define clear runtime configuration and handle secrets outside images.

I/O & resources

  • Application code and build scripts
  • Base image or runtime stack
  • Registry access and CI/CD pipeline
  • Versioned container image
  • Deployment manifests for orchestration
  • Monitoring metrics and logs per instance

Description

Containerization packages applications and their runtime dependencies into isolated, portable containers based on standardized images and runtimes. It enables consistent deployment across environments, faster delivery, and resource-efficient workload consolidation. Container orchestration and image management are typical operational concerns requiring governance, security and CI/CD integration.

✔

  • Portability across development, test and production environments
  • Faster delivery and reproducible deployments
  • Improved utilization and workload density

✖

  • Overhead from image sizes and startup times
  • Not automatically more secure without additional measures
  • Persistent state requires additional architectural decisions

  • Image size

    Average size of produced container images; affects startup time and storage needs.

  • Startup time

    Time until a container instance becomes ready; relevant for scaling and resilience.

  • Deployment lead time (CI→Prod)

    Time from commit to successful production deployment; measures release velocity.

Docker for microservices

Use of Docker images to package individual microservices, registry usage and CI/CD integration.

OCI images in enterprise registry

Standardized OCI images are managed and versioned in a private registry.

Kubernetes orchestration

Deployment of containerized workloads on Kubernetes using Deployments, Services and ConfigMaps.

1

Analyze existing application dependencies and runtime requirements.

2

Create consistent Dockerfiles/OCI manifests and minimal base images.

3

Set up a private registry and CI/CD integration for automated builds.

4

Implement image scanning, signing and governance policies.

5

Incremental migration of services and validation in staging environments.

6

Introduce monitoring, logging and automated recovery in production.

⚠️ Technical debt & bottlenecks

  • Legacy images without update process
  • Untagged images in the registry
  • Missing automation for security updates of base images
Image sizeStartup timeNetwork latency between containers
  • A team packs debug tools into production images, increasing attack surface.
  • Using containers as a substitute for missing architectural separation instead of proper modularization.
  • Uncontrolled pushing of nightly images into the production registry.
  • Forgetting to clean temporary layers leads to large images.
  • Different base image versions across teams create drift.
  • Insufficient limits and requests in the orchestrator cause instability.
Container build and image optimizationOrchestration and deployment strategiesSecurity and networking concepts in container environments
Portability across environmentsFast, repeatable deploymentsIsolation and consistency of runtimes
  • • Existing legacy systems may hinder migration
  • • Regulatory requirements for persistence and logging
  • • Limited resources in edge or embedded environments