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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.

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

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

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.

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

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

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

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

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

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

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

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

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.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Time-based partitioning organizes time-series data into smaller, efficient segments for better querying.

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.

Read-after-write consistency ensures users see their latest updates immediately.

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

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

Graceful degradation allows apps to function partially during service failures.