In the 28th episode, we go over Burton Bloom's Bloom filter from 1970, a groundbreaking data structure that enables fast, space-efficient set membership checks by allowing a small, controllable rate of false positives.Unlike traditional methods that store full data, Bloom filters use a compact bit array and multiple hash functions, trading exactness for speed and memory savings.

This idea transformed modern data science and big data systems, powering tools like Apache Spark, Cassandra, and Kafka, where fast filtering and memory efficiency are critical for performance at scale.