Distributed Systems
Defines distributed systems as software that coordinates independent components over unreliable networks to deliver a single, dependable experience. The field addresses latency, failures, and concurrency as first-class constraints, trading off consistency, availability, and partition tolerance with clear intent. Architecture choices are expressed through interfaces, contracts, and topology rather than monolith boundaries.
Core elements include communication patterns (request/response, pub-sub, streams), storage strategies (sharding, replication, eventual and strong consistency), and consensus (Raft, Paxos) where agreement matters. Cloud primitives and container orchestration place compute near demand, while edge and IoT extend computation to sensors and actuators. Security spans identity, encryption, and zero-trust networking; observability ties together logs, metrics, and traces to make complex behavior legible.
The outcome is resilience at scale. Systems degrade gracefully under load, recover automatically from faults, and evolve without downtime. Teams gain the vocabulary to reason about CAP tradeoffs, locality, and throughput, building platforms that are fast, safe, and adaptable.