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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.

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

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

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

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

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

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

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

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

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

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

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

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

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

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

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

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

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

Database per Service ensures each microservice owns its data, reducing dependencies.

Disaster recovery ensures systems can restore services and data after major disruptions.

Functional requirements define what a system does, while non-functional requirements define how it performs.