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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

Distributed locks ensure only one server performs a task, avoiding duplication.

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

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

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

Stateless servers are easier to scale because they don't store user session data locally.

API pagination helps manage large datasets by breaking them into smaller parts.

Cache stampede occurs when many requests hit the database due to a cache miss.