Hash tables (also called hash maps or associative arrays) are data structures that implement an associative array abstract data type, mapping keys to values for rapid lookup. At their core, they use a hash function to compute an index into an array of buckets from which the desired value can be retrieved. Hash tables are ubiquitous in computer science—powering dictionaries in Python, maps in Go, HashMaps in Java, unordered_map in C++, and countless database indexes and caches. The magic lies in achieving O(1) average-case time complexity for insertions, deletions, and lookups, though the worst case remains O(n) when collisions overwhelm the structure. Understanding how hash tables manage collisions, resize dynamically, and balance speed with memory efficiency is essential for writing high-performance code. A key insight: the performance of a hash table is determined as much by the quality of its hash function and collision resolution strategy as by the underlying array itself.