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

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

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

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

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

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

Database replication enhances data availability and read performance but has challenges like replication lag.

HLD provides an overview of system architecture, while LLD details implementation specifics.

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

Choose Elasticsearch for complex text searches, but use databases for structured queries.

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

Fault domains help prevent multiple servers from failing together in an architecture.

Elasticsearch uses shards for data distribution and replicas for redundancy.

Choosing the right distributed lock depends on your system's needs and existing tools.

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

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