Scalability types (vertical, horizontal, diagonal), Stateless vs. stateful services, Load balancing algorithms (round-robin, least connections, IP hash), Layer 4 vs. Layer 7 load balancing, Health checks and graceful...
Scalability types (vertical, horizontal, diagonal), Stateless vs. stateful services, Load balancing algorithms (round-robin, least connections, IP hash), Layer 4 vs. Layer 7 load balancing, Health checks and graceful degradation, Auto-scaling policies (CPU/memory utilization, custom metrics, predictive scaling), Capacity planning and right-sizing.
Docker containerization best practices, Kubernetes architecture (control plane, worker nodes, etcd), Pod lifecycle and controllers (Deployment, StatefulSet, DaemonSet), Kubernetes Services (ClusterIP, NodePort, LoadBalancer, ExternalName), Ingress controllers and API gateways, Service mesh patterns (Istio virtual services, traffic management, observability).
Message brokers (Kafka, RabbitMQ, SQS), Pub-sub patterns and fan-out/fan-in, Event sourcing and CQRS principles, Apache Kafka (topics, partitions, consumer groups, exactly-once semantics), Stream processing (Kafka Streams, KSQL), Serverless event sources (Lambda triggers, Cloud Functions events), Saga pattern for distributed transactions.
Caching patterns (cache-aside, write-through, read-through), Redis cluster architecture and data sharding, Memcached consistent hashing, CDN integration for static assets, NoSQL selection criteria (DynamoDB, Cassandra, MongoDB Atlas), Relational database scaling (read replicas, sharding), Multi-region database replication.
The three pillars (metrics, logs, traces), Prometheus/Grafana monitoring stacks, OpenTelemetry instrumentation, Distributed tracing analysis, Chaos engineering (Chaos Monkey, Gremlin), SLO/SLI/SLA definition and error budgets, FinOps practices, Reserved/spot instances, Cost allocation tagging, Multi-cloud cost comparison.