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

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.

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

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.

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.

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.

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

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

Encryption at rest and in transit are essential for protecting data both stored and in motion.

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

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

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

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.

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.

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

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

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.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

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.

Cassandra uses a masterless architecture to distribute and replicate data efficiently.

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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