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

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

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

Choose Elasticsearch for complex text searches, but use databases for structured queries.

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

CQRS separates how we change data from how we read data, improving scalability.

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.

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

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

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

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

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

Search engines collect, index, retrieve, and rank documents to provide relevant results.

Availability is about access; reliability is about correct performance.

Functional requirements define what a system does, while non-functional requirements define how it performs.

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

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

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