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A generic, event-driven pattern to enforce retention policies: detect expired data, publish cleanup events, run idempotent workers, then update metadata and storage with a complete audit trail.
Evaluates retention rules, selects eligible partitions/files, and publishes cleanup events.
Decouples detection from execution and enables scalable, reliable processing with retries.
Consume events and perform cleanup in parallel across metadata and storage layers.
Stores schemas and partition state. Workers update catalog state during cleanup.
Data files reside here. Workers delete files based on retention policy decisions.
Captures what was deleted, when, and why—supporting compliance and investigations.
Retention policies execute automatically with no manual cleanup or operational overhead.
Reduces total storage footprint and cloud spend by continuously removing expired data.
Creates a full audit trail of what was deleted, when, and why for governance needs.
Event-driven workers scale out to handle growth without re-architecting the system.
Idempotent operations allow safe retries and fault tolerance during failures.
Rate limits, batching, and backpressure protect storage systems and prevent runaway deletes or production impact.
Note: This page is intentionally generic (no vendor or internal system names). Swap in your platform equivalents as needed.