- An understanding of basic networking and system design concepts
- Experience with general software development practices
- Familiarity with cloud computing and API design is helpful
Audience
- Software architects and technical leads
- Backend engineers and DevOps professionals
- System designers building scalable cloud applications
Distributed and Highly Scalable Systems architecture is the design approach used to build reliable, fault-tolerant, and performant systems that operate across multiple nodes and regions.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level software professionals who wish to understand how to architect distributed systems that scale efficiently and handle failure gracefully.
By the end of this training, participants will be able to:
- Design distributed systems that handle load, latency, and failure.
- Understand key architectural patterns such as microservices, event-driven architecture, and CQRS.
- Evaluate trade-offs between consistency, availability, and partition tolerance (CAP theorem).
- Choose appropriate communication, storage, and coordination strategies.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Introduction to Distributed Systems
- What is a distributed system?
- Common challenges: latency, consistency, 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
- CAP theorem explained
- Strong vs. eventual consistency
- Choosing 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
- Using 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
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