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

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

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

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

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

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

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

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

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.