Reliability and Operations
High availability, fault tolerance, scaling, backups, disaster recovery, monitoring, and what an SLA actually promises.
A perfectly secure system that goes down still fails everyone relying on it. This topic turns from who protects your cloud resources to how they stay running: the redundancy patterns that survive a hardware failure, the automation that matches capacity to demand, the plan for when a failure is bigger than redundancy alone can absorb, and the way teams actually find out something went wrong.
What This Topic Covers
- high availability and fault tolerance, and why a brief automated recovery and zero visible interruption are 2 different, differently priced guarantees
- active-active and active-passive redundancy, and how spreading compute and databases across Availability Zones removes a single point of failure
- scalability and elasticity, and why a system's ability to grow is not the same as it automatically growing and shrinking to match real-time demand
- Auto Scaling group mechanics: minimum, desired, and maximum capacity, and target tracking, step, and scheduled scaling policies
- Recovery Time Objective and Recovery Point Objective, and the 4 disaster recovery strategies, from backup and restore through multi-site active/active, that trade cost for recovery speed
- monitoring with metrics, logs, and alarms, and the difference between an SLI, an SLO, and an SLA
Why It Matters
Every pattern in this topic answers a version of the same question: what happens when something fails anyway. Hardware fails at 2am regardless of how good the code is, so multi-AZ redundancy and the high-availability-versus-fault-tolerance distinction decide whether that failure is invisible or just survivable. Demand never stays flat, so scalability and elasticity decide whether a team pays for its worst day year-round or only when it actually arrives. Redundancy inside a Region eventually meets a failure bigger than it was built for, which is exactly what Recovery Time Objective, Recovery Point Objective, and a tested disaster recovery strategy exist to bound.
None of it means anything without a way to find out a failure happened and a way to state, in numbers, how reliable a system actually was. That is what monitoring and SLAs close the topic with: metrics and alarms that catch a problem in minutes instead of from a customer complaint, and a clear-eyed read of what a provider's SLA compensates and what it never covers. Together, this topic gives you the vocabulary a cloud job interview and this course's certification path both expect: not just how to build a resilient system, but how to prove, with numbers, that it stayed that way.