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

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

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

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

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

Retention policies help manage how long data is kept in databases.

Distributed backpressure helps manage the flow of work between services to prevent overload.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

Backpressure prevents system overload by managing message processing rates in Kafka.

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

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

Kafka consumer offsets help track message processing to avoid loss and duplication.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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