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The choice between SQL and NoSQL databases depends on your application's specific needs.

Data warehouses help businesses analyze large amounts of data for better decision-making.

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

Time-series databases are designed to store and query time-stamped metrics efficiently.

OLTP handles transactions while OLAP is for data analysis and reporting.

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.

Downsampling and aggregation reduce data volume while preserving essential trends.

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.

Sharding splits databases into smaller parts to improve performance and scalability.