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Container data can be lost unless stored in persistent volumes.

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

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

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

Stateless servers are easier to scale because they don't store user session data locally.

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

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

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

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

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

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

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

Choosing the right distributed lock depends on your system's needs and existing tools.

Point-in-Time Recovery allows databases to be restored to a specific moment before data loss.

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

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

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