Cloud Fundamentals

The veteran JMS message broker — protocol-polyglot, deeply embedded in Java estates, and the 'Classic vs Artemis' fork every operator must understand.

Amazon Resource Names — the globally unique addressing scheme for every AWS resource, and the string format that IAM policies are actually written against.

An AWS service that continuously records your resource configurations and evaluates them against rules — answering 'what changed, when, and is it compliant?' for audit and drift detection.

AWS's service for managing many accounts as one org — consolidated billing plus guardrails (SCPs) that cap what accounts can do. The foundation of a multi-account strategy.

AWS's managed multi-account landing zone: automated account creation with guardrails, centralized logging, and organization-wide policy from day one.

Storing state as an immutable log of events rather than current values — a perfect audit trail and time machine, bought with real complexity most systems don't need.

Systems that communicate by emitting and reacting to events rather than calling each other directly — loose coupling and scale, bought with eventual consistency and debugging difficulty.

The retry discipline that prevents failed calls from becoming outages: wait longer after each failure, add randomness, and give up on purpose.

The discipline of making cloud spend an engineering metric — visibility, accountability, and optimization as a continuous loop, not a quarterly panic.

Deliberate randomness added to timing — retries, cron schedules, cache TTLs — to break the synchronization that turns many well-behaved clients into an accidental DDoS.

A pre-configured, secure, multi-account cloud foundation set up before you deploy workloads — accounts, networking, identity, logging, and guardrails done right from the start so you don't retrofit governance later.

An asynchronous buffer between services: producers enqueue work, consumers process at their own pace — decoupling that turns traffic spikes into backlogs instead of outages.

A disaster recovery strategy keeping critical data replicated while compute resources remain inactive until failover.

A disaster recovery pattern maintaining replicated data and minimally scaled compute resources for fast failover.