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Course Outline

Introduction to Distributed Systems

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

Scalability Principles

  • Vertical vs. 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

  • Explanation of the CAP theorem.
  • Strong vs. 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 basic 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 working on scalable cloud applications.
 21 Hours

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