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

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

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

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.

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

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

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

Database replication helps keep data available and allows systems to handle more read requests.

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

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