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

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

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

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

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

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

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

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

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

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

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

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

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Late-arriving events can be dropped, updated, or routed based on system needs.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.