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Caching helps applications respond faster by serving data from memory instead of hitting the database.

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Redis is a fast in-memory data store used for caching and low-latency applications.

Cache eviction policies determine which data to remove when caches are full.

Write-Ahead Log ensures data is safely recorded before changes are made.

Kafka uses state stores to remember previous events in stateful stream processing.

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

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

Database indexing speeds up data retrieval by allowing quick lookups.

Cache invalidation is about knowing when to refresh cached data.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

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

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