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

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

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

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

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

Connection pooling reuses database connections but can cause issues if misconfigured.

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

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

Cache invalidation is about knowing when to refresh cached data.

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

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