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

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

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

REST API design focuses on creating clean, efficient, and scalable web services.

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

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

Transactions ensure database operations are reliable using ACID properties.

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

Choosing between a shared database and a database per service affects system design significantly.

Retention policies help manage how long data is kept in databases.

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

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

Time-based partitioning organizes time-series data into smaller, efficient segments for better querying.

Secure API design requires multiple strategies to protect sensitive data and access.

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

B-Trees help databases find data quickly without scanning every row.

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

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

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.