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

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.

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

Container data can be lost unless stored in persistent volumes.

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.

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.

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.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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.