Coralogix explores the challenges of cardinality estimation in Big Data contexts, highlighting constraints related to time complexity, space complexity, and distributed processing. The blog introduces HyperLogLog, a cardinality estimation algorithm that uses hash functions to estimate the number of unique elements in large datasets with 98% accuracy and minimal memory usage. HyperLogLog employs stochastic averaging, using multiple registers for increased accuracy and reduced error variance. The algorithm is particularly suited for distributed processing, allowing results from different machines to be combined with minimal coordination. The blog also critiques Python's hash() function, recommending the use of more effective non-cryptographic hash functions like Murmur3. HyperLogLog is presented as an efficient solution for managing Big Data, offering precision and scalability with a small memory footprint.