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Course Outline

Introduction to Distributed Systems

  • What defines a distributed system?
  • Common challenges: latency, consistency, availability
  • Overview of system components and communication models

Scalability Principles

  • Vertical versus horizontal scaling
  • Load balancing and elasticity
  • Scaling storage, compute, and I/O

Architectural Patterns

  • Client-server and multi-tier architectures
  • Service-oriented and microservice architectures
  • Event-driven architecture and message queues

CAP Theorem and Consistency Models

  • CAP theorem explained
  • Strong versus eventual consistency
  • Selecting between consistency and availability

Data Distribution and Storage Strategies

  • Partitioning and sharding
  • Replication strategies and quorum reads/writes
  • Distributed databases and key-value stores

Communication and Coordination in Distributed Systems

  • REST, gRPC, message brokers (e.g., Kafka, RabbitMQ)
  • Leader election and distributed consensus
  • Utilizing Zookeeper or etcd for coordination

Fault Tolerance and Reliability

  • Designing for failure and graceful degradation
  • Retry mechanisms, timeouts, and circuit breakers
  • Monitoring, observability, and chaos engineering

Cloud-Native and Modern Implementation Practices

  • Containers, orchestration, and Kubernetes
  • Statelessness and immutability
  • Best practices for distributed system security

Summary and Next Steps

Requirements

  • A solid understanding of fundamental networking and system design concepts.
  • Experience with general software development practices.
  • Familiarity with cloud computing and API design is advantageous.

Audience

  • Software architects and technical leads.
  • Backend engineers and DevOps professionals.
  • System designers focused on building scalable cloud applications.
 21 Hours

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