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Data residency is crucial for compliance with laws about where data can be stored.

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.

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

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

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

Redis is a fast in-memory data store used for caching and low-latency applications.

Distributed caching spreads data across multiple servers for better performance and reliability.

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

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

Cache penetration occurs when requests for non-existent data overload the database.

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

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Cache invalidation is about knowing when to refresh cached data.

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

Database per Service ensures each microservice owns its data, reducing dependencies.

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

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

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

Classifying failures and using controlled retries in Kafka prevents outages.