**Why Bloom filters matter**: Probabilistic set membership—O(1) lookup, no false negatives, tunable false positive rate. **Use case**: Large-fact/small-dimension join—build Bloom filter of dimension keys, broadcast to executors; prune fact partitions before shuffle. Example: 1B fact rows, 10M dimension—Bloom filter of 10M keys ~10MB; most fact partitions prune early. **Scalability trade-offs**: False positives grow with size; tune FPR (e.g., 1%) vs memory....
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