AWS Services Cheat Sheet
AWS services grouped by compute, storage, networking, data, and operations roles.
Cloud infrastructure, distributed coordination, and the reliability tradeoffs behind scaling services across machines, zones, and regions.
AWS services grouped by compute, storage, networking, data, and operations roles.
Cloud data pipeline stages across ingestion, storage, processing, and serving.
Cloud native systems assume elastic infrastructure, automation, and failure.
High-availability design through redundancy, failover, and degraded operation.
System design blueprint for turning workload constraints into architecture choices.
CAP, BASE, SOLID, and KISS as tradeoff frameworks across system design layers.
Azure services grouped by compute, data, networking, identity, and operations roles.
AWS evolution from core infrastructure primitives to managed platform services.
Cloud load balancer choices across protocol layers, routing, and failure scope.
Cloud cost reduction through rightsizing, lifecycle controls, and usage visibility.
Cloud bills grow through idle resources, egress, request pricing, and retained data.
Core AWS services grouped by identity, networking, compute, storage, and data.
Cloud messaging patterns for buffering, fan-out, async work, and cross-service coordination.
Distributed locks enforce single-owner work across nodes with expiring leases.
Cloud monitoring signals, storage, alerting, and response across providers.
Retry policies for transient failures, backoff, idempotency, and load control.
Five ID generation patterns and the tradeoffs between order, scale, and coordination.
The 12-Factor App principles for deployable, configurable, and scalable services.
Event sourcing through append-only logs, projections, and replayable state.
Configuration management through versioned state, IaC, and secret separation.
Infrastructure as Code tools across provisioning, configuration, and packaging.
Amazon Brazil builds through version sets, package isolation, and reproducibility.
Distributed ID generation tradeoffs across uniqueness, ordering, and scale.
Typical AWS network architecture with VPC, subnet, routing, and edge boundaries.
Fault-tolerant system design through isolation, redundancy, and controlled failure.
Web request error handling through status codes, retries, and failure boundaries.
Monorepos and microrepos compared by coordination cost, tooling, and team autonomy.
Core system design building blocks for scaling, coordination, and reliability.
Architectural scalability through bottleneck analysis, stateless tiers, and partitioning.
Twenty-five papers that shaped computing theory, systems, and networking.
Resiliency patterns that isolate failures, absorb load, and preserve service.
Website scaling through stateless services, caching, replicas, and partitioning.
Lambda stays fast through pooled capacity, microVMs, and warm reuse.
System design balances scalability, availability, reliability, and performance.
Leap second handling in distributed systems with smearing and clock consistency.
Distributed failure detection through heartbeats, suspicion, and quorum signals.
Idempotency patterns for safe retries in writes, payments, messaging, and scheduled work.
Architecture patterns for request flow, event fan-out, queues, and peer coordination.
Vertical and horizontal partitioning compared by row, column, and scaling needs.
Cloud disaster recovery through RTO, RPO, backups, and failover patterns.
System design tradeoffs and scaling patterns summarised in one cheat sheet.
Eight scalability strategies across caching, partitioning, replication, and async work.
AWS Lambda execution through event routing, microVM isolation, and warm reuse.