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

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

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.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

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

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

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.

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

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