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Batch processing collects data to process later, while stream processing handles data in real-time.

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

Late-arriving events can be dropped, updated, or routed based on system needs.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

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

Data residency is crucial for compliance with laws about where data can be stored.

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

CDC with Kafka allows real-time updates from databases to multiple services.

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

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

Kafka can help ensure messages are processed once, but requires careful design.

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

Tokenization and analyzers help search engines process and match text effectively.

Search engines collect, index, retrieve, and rank documents to provide relevant results.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Tumbling windows are non-overlapping time segments, while hopping windows can overlap.