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Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

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

Kubernetes Ingress allows one entry point for multiple applications, simplifying routing.

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

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.

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Backpressure helps manage message flow in Kafka during traffic spikes.

Choosing the right boundaries for microservices is crucial for effective architecture.

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