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

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

Elasticsearch ILM automates data management through defined lifecycle phases.

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

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

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

Replicas keep your system available, while snapshots allow for data recovery.

Lamport Timestamps help order events in distributed systems while preserving causality.

In distributed systems, timestamps don't always reflect the true order of events.