Similarity measurement is a fundamental concept in Big Data analytics that determines how closely two data objects resemble each other. It is widely used in data mining, machine learning, clustering, classification, recommendation systems, plagiarism detection, document analysis, social network analysis, and image retrieval. As Big Data involves massive volumes of structured and unstructured data, efficient similarity measures help identify patterns, relationships, and hidden knowledge. The selection of a similarity measure depends on the nature of the data, whether binary, numerical, textual, or high-dimensional. One of the most widely used techniques is Jaccard Similarity. It measures the similarity between two sets by comparing the size of their intersection with the size of their union. It is mathematically expressed as: J(A,B) = |A ∩ B| / |A ∪ B| The value ranges from 0 to 1, where 0 indicates no common elements and 1 indicates identical sets. Jaccard similarity is particularly suitable for binary attributes, sparse datasets, market basket analysis, document comparison, and recommendation systems where user preferences are represented as sets of purchased or liked items. For example, if User A has watched five movies and User B has watched four movies, with three movies common between them, the Jaccard similarity is calculated as 3/6 = 0.5. Higher similarity suggests similar preferences. Another important technique is Cosine Similarity, which measures the cosine of the angle between two vectors rather than their magnitude. It is represented as: Cosine Similarity = (A · B) / (||A|| × ||B||) The similarity value ranges from 0 to 1 for non-negative vectors. A value close to 1 indicates that the vectors point in nearly the same direction, implying strong similarity. Cosine similarity is extensively used in text mining, natural language processing, document retrieval, and high-dimensional datasets such as