Catalog
concept#Architecture#Software Engineering#Platform#Reliability

Software Architecture

Conceptual description of a software system's structure, components and interfaces to satisfy functional and non-functional requirements.

Software architecture defines a system's structure, components, interfaces and guiding principles for designing complex software.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

CI/CD pipelines (e.g. Jenkins, GitHub Actions)Monitoring and observability tools (e.g. Prometheus, Grafana)Cloud platforms and PaaS (e.g. Azure, AWS)

Principles & goals

Clearly defined interfaces and responsibilitiesAdherence to quality attributes (scalability, security, maintainability)Document architecture decisions and their rationale
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Lack of alignment leads to silos and inconsistencies
  • Incorrect requirement assumptions can cause costly rework
  • Insufficient operationalization of architecture hinders runtime stability
  • Early and iterative architecture validation with prototypes
  • Document rationale for decisions, not just outcomes
  • Close collaboration between architecture and product teams

I/O & resources

  • Business and product goals
  • Non-functional requirements
  • Existing system architecture and tech stack
  • Architecture diagrams and component descriptions
  • Documented architecture decisions (ADRs)
  • Quality and operational requirements

Description

Software architecture defines a system's structure, components, interfaces and guiding principles for designing complex software. It creates abstractions to achieve scalability, maintainability and reuse and makes quality requirements explicit. Architectural decisions shape technology, operations and organizational aspects across enterprise, domain and team levels.

✔

  • Improved scalability and performance through clear system structure
  • Increased maintainability and reduced change risk
  • Better communication basis between engineering and business

✖

  • High initial effort for analysis and documentation
  • Over-architecting can slow time-to-market
  • Not all quality attributes can be optimized simultaneously

  • Mean Time To Recovery (MTTR)

    Average time to recover after an outage.

  • Deployment frequency

    How often releases or deployments occur.

  • Latency and response time

    Runtime performance metrics of endpoints and processes.

Microservice adoption in e-commerce

Splitting an order system into autonomous services to improve scalability and release frequency.

Event-driven architecture in finance

Use of event streams to decouple components and ensure traceability.

Monolithic consolidation for performance

Targeted consolidation of distributed components to reduce latency and operational overhead.

1

Collect goals and quality requirements

2

Analyze domains and bounded contexts

3

Evaluate architecture patterns and styles

4

Document architecture decisions (ADRs)

5

Operationalize: introduce monitoring, deployments and SLAs

⚠️ Technical debt & bottlenecks

  • Unrefactored monolith parts after split
  • Outdated interfaces without versioning
  • Missing automated tests for critical integrations
Monolithic database as bottleneckNon-standardized interfacesInsufficient monitoring
  • Introducing microservices for small, non-critical functions without an operational plan
  • Ignoring non-functional requirements during design phase
  • Documentation rot: architecture is no longer up to date
  • Confusing architecture principles with implementation details
  • Neglecting operations and observability
  • Late involvement of relevant stakeholders
System and domain modelingTechnical decision making and trade-off analysisCommunication and facilitation skills for stakeholders
ScalabilityMaintainabilitySecurity
  • • Budget and time constraints
  • • Regulatory requirements
  • • Existing legacy systems