Platform Engineering
Platform Engineering is an approach to creating and managing platforms that support the development and delivery of software solutions.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityAdvanced
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Technological obsolescence
- Security risks from external dependencies
- Lack of user acceptance
- Regular review of platform performance
- Use of automation tools
- Close collaboration between development and operations
I/O & resources
- Technical requirements
- User feedback
- Market research
- Functional platform
- User-friendly interfaces
- Scalable solutions
Description
Platform Engineering involves the design, implementation, and maintenance of platforms that serve as a foundation for software applications. It aims to improve the efficiency and scalability of development processes by providing standardized tools, practices, and architectures.
✔Benefits
- Increased efficiency in software development
- Better collaboration between teams
- Faster time-to-market for products
✖Limitations
- High initial investments
- Complexity in management
- Dependency on specific technologies
Trade-offs
Metrics
- Development Time
Time taken to develop new features.
- Error Rate
Number of errors per 1000 lines of code.
- Customer Satisfaction
Rating of user satisfaction with the platform.
Examples & implementations
Use of Kubernetes for Container Orchestration
A company uses Kubernetes to orchestrate containers and automate the deployment of microservices.
Implementation of a CI/CD Pipeline
A company implements a CI/CD pipeline to automate the software delivery process.
Use of Cloud Services for Scalability
A company uses cloud services to increase the scalability of its applications.
Implementation steps
Conduct requirements analysis
Create architecture design
Carry out development and testing
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated technologies
- Insufficient documentation
- Technical debt from quick fixes
Known bottlenecks
Misuse examples
- Use of unsupported technologies
- Disregard for security standards
- Insufficient testing before deployment
Typical traps
- Assumption that all users use the platform the same way
- Over-optimization without real data
- Neglect of long-term maintenance
Required skills
Architectural drivers
Constraints
- • Compliance with security standards
- • Regulatory requirements
- • Technological compatibility