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CDC with Kafka allows real-time updates from databases to multiple services.

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

Consistency models determine what data reads in distributed systems can see.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

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

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

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

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

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

Elasticsearch has different node roles that help manage data and scale clusters effectively.

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