Bloom filters are a space-efficient probabilistic data structure used to test whether an element is part of a set, allowing for false positives but no false negatives. The technology, while older, remains relevant and is commonly used in various applications due to its efficiency in terms of space complexity. Users express curiosity about related technologies, like data sketches and streaming algorithms, highlighting alternatives that may outperform Bloom filters in specific scenarios, such as k-Minimum values for cardinality estimation.