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Platform Infrastructure

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Build and scale the platforms that power modern applications.

📋 What You'll Learn​

This section covers modern platform infrastructure patterns:

  1. Distributed Systems - Consistency, availability, partition tolerance

🎯 Interview Focus Areas​

Critical Topics​

Platform infrastructure interviews often focus on:

  1. System Design (95% of interviews)

    • Scalability patterns
    • Data consistency models
    • Failure handling
    • Performance optimization

  2. Distributed Systems (85% of interviews)

    • CAP theorem application
    • Consensus algorithms
    • Distributed transactions
    • Event-driven architecture

  3. Data Architecture (80% of interviews)

    • SQL vs NoSQL trade-offs
    • Caching strategies
    • Data partitioning
    • Replication models

  4. Modern Patterns (70% of interviews)

    • Microservices communication
    • Service mesh benefits
    • API gateway patterns
    • Event streaming

📚 Essential Concepts​

Distributed Systems Fundamentals​

CAP Theorem

  • Consistency: All nodes see the same data
  • Availability: System remains operational
  • Partition Tolerance: System continues despite network failures
  • You can only guarantee 2 out of 3

Consistency Models

  • Strong consistency
  • Eventual consistency
  • Weak consistency
  • Causal consistency

Data Storage Patterns​

SQL Databases

  • ACID properties
  • Strong consistency
  • Complex queries
  • Vertical scaling challenges

NoSQL Databases

  • Document stores (MongoDB)
  • Key-value stores (Redis)
  • Column-family (Cassandra)
  • Graph databases (Neo4j)

Communication Patterns​

Synchronous

  • REST APIs
  • GraphQL
  • gRPC

Asynchronous

  • Message queues
  • Event streaming
  • Pub/sub systems

💡 Interview Strategies​

When Discussing Infrastructure​

  1. Start with requirements

    • Scale expectations
    • Consistency needs
    • Latency requirements
    • Cost constraints

  2. Consider trade-offs

    • Performance vs cost
    • Consistency vs availability
    • Complexity vs maintainability

  3. Think about operations

    • Monitoring and debugging
    • Deployment strategies
    • Disaster recovery
    • Team expertise

Common Pitfalls to Avoid​

  • Over-engineering for scale you don't need
  • Ignoring operational complexity
  • Not considering data consistency
  • Forgetting about failure modes
  • Underestimating costs

🔧 Hands-On Skills​

Before interviews, you should be able to:

  • Design a distributed system handling 1M requests/second
  • Choose appropriate database for different use cases
  • Implement caching strategies
  • Design event-driven architectures
  • Set up CI/CD pipelines
  • Configure service mesh

📊 Technology Landscape​

Popular Platforms & Tools​

Databases

  • PostgreSQL, MySQL (relational)
  • MongoDB, DynamoDB (document)
  • Redis, Memcached (cache)
  • Cassandra, ScyllaDB (wide column)
  • Kafka, Pulsar (streaming)

Service Mesh

  • Istio
  • Linkerd
  • Consul Connect
  • AWS App Mesh

API Gateways

  • Kong
  • Traefik
  • AWS API Gateway
  • Nginx Plus

CI/CD

  • Jenkins
  • GitLab CI
  • GitHub Actions
  • ArgoCD
  • Tekton

🚀 Study Path​

Quick Review (1 week)​

  • Day 1-2: Distributed systems theory
  • Day 3-4: Database patterns
  • Day 5: Message queues and streaming
  • Day 6-7: Service mesh and API gateways

Deep Dive (2-3 weeks)​

  • Week 1: Master distributed systems concepts
  • Week 2: Database internals and scaling
  • Week 3: Modern platform patterns

📖 Must-Read Resources​

🎓 Next Steps​

  1. Start with Distributed Systems for fundamentals
  2. Review the technology landscape section above for database patterns
  3. Study the communication patterns section for async architectures
  4. Explore service mesh and API gateway concepts in the technology section

Remember: Platform infrastructure is about building reliable, scalable foundations. Always consider operational aspects, not just technical elegance.