Implement dynamic month-end event tracking instead of static 1-hour window

Problem

Currently we process previous month data until a static 1-hour window after month change. We need a more organic/dynamic way to identify when delayed events have arrived before starting consumption of new month data.

Current State

• Static 1-hour window after month boundary
• Arbitrary cutoff may miss delayed events or wait unnecessarily
• No tracking of delayed event patterns

Proposed Solution

• Analyze delayed event patterns at month-end
• Implement dynamic window based on historical data
• Track event arrival times to determine optimal cutoff

Acceptance Criteria

• Analyze historical delayed event data
• Identify patterns in event arrival times
• Design dynamic window algorithm
• Implement dynamic month-end handling
• Add monitoring for delayed events
• Document month-end event handling process