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

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.

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

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.

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

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

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

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

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

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

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

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

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

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

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

Kafka commit strategies determine how offsets are managed during message processing.

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.

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

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

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

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

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.

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

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

Cache eviction policies determine which data to remove when caches are full.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

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

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

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.

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

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.

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

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

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

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

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

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

Elasticsearch uses master election and quorum to prevent split brain scenarios.

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

Graceful degradation allows apps to function partially during service failures.