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

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

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

Cassandra uses a masterless architecture to distribute and replicate data efficiently.

Apache Kafka is a system for managing large volumes of events efficiently.

MongoDB uses sharding to distribute data across multiple servers for better scalability.

Kafka uses partitions to allow multiple consumers to process messages in parallel.

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

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

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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