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Disaster recovery ensures systems can restore services and data after major disruptions.

Disaster Recovery Testing verifies that your recovery plan works effectively in real situations.

RPO and RTO are key metrics for planning disaster recovery strategies.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Replicas keep your system available, while snapshots allow for data recovery.

Point-in-Time Recovery allows databases to be restored to a specific moment before data loss.

Choosing between single and multi-region architecture affects system resilience and cost.

Active-passive architecture ensures a standby system takes over if the main system fails.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Global failover ensures applications remain available by redirecting traffic from failed regions to functioning ones.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

High Availability ensures your application remains operational even during server failures.

Backup strategies include Full, Incremental, and Differential, each with unique benefits.

Data residency is crucial for compliance with laws about where data can be stored.

Dynamo-style databases are designed for high availability and fault tolerance in distributed systems.

Database replication helps keep data available and allows systems to handle more read requests.

Kafka consumers resume processing from the last committed offset after a crash.

Fault domains help prevent multiple servers from failing together in an architecture.

Availability Zones help prevent downtime by isolating failures across multiple locations.

Fallbacks allow systems to handle failures safely and maintain user experience.

Cross-region database replication keeps databases synchronized across different geographical locations.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Partial failure means some services fail while others keep running, impacting system reliability.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka lag recovery time is how fast consumers can process backlogged messages.

Active-Active Architecture uses multiple regions to serve traffic simultaneously for better resilience.

Write-Ahead Log ensures data is safely recorded before changes are made.

Graceful degradation allows apps to function partially during service failures.

N+1 redundancy means having one extra server to maintain capacity during a failure.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

A single component's failure can take down your entire application, even with redundancy.

Database replication enhances data availability and read performance but has challenges like replication lag.

Hinted Handoff allows a database to manage temporary replica failures by storing missed write operations.

Change Data Capture allows real-time syncing of database changes to other systems.

Crash failures stop a service, while network failures disrupt communication.

CDC with Kafka allows real-time updates from databases to multiple services.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

Kafka uses replication and in-sync replicas to ensure data is always available.

You can replay Kafka events safely by separating live and replay processes.

Retry storms occur when too many retries overload a failing service.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

Kubernetes uses Pods, ReplicaSets, and Deployments to manage application availability and updates.

Failover ensures systems remain operational by managing leader changes during failures.

Read replicas allow databases to handle more read requests by distributing them across multiple copies.