A detailed taxonomy of data change events used in Change Data Capture (CDC) systems, covering three types: full events (complete record state), delta events (only changed fields), and id-only events (just the primary key). Each type is analyzed for structure, use cases, trade-offs, and compatibility with downstream systems. Full events are easiest to consume and support log compaction; delta events save space but require partial-update-capable sinks or stateful stream processing for re-hydration; id-only events are compact but risk missing intermediary updates and suit only narrow use cases like cache invalidation. The post also covers change event metadata (timestamps, transaction IDs, log positions) and how tools like Apache Flink can bridge gaps between event types in data pipelines.
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