Explore probabilistic data structures through a practical conference talk that uses a dataset of 112,092 UFO sightings to demonstrate real-world applications. Learn how these memory-efficient data structures provide approximate answers to complex queries while using significantly less space than traditional exact algorithms. Discover the mathematical foundations behind structures like Bloom filters, HyperLogLog, and Count-Min Sketch, and understand when to choose probabilistic approaches over deterministic ones. Examine how these structures handle massive datasets by trading perfect accuracy for substantial memory savings and improved performance. Gain insights into the trade-offs between precision and efficiency, and see practical examples of how major tech companies use these structures to solve big data challenges at scale.