Cost-Effective High-Cardinality Data Stream Processing: A Comparative Analysis of Probabilistic Data Structures for Enterprise-Scale Applications

High-cardinality data stream processing presents significant computational and economic challenges for enterprises managing billions of daily events. Traditional exact counting exhibits linear memory growth and quadratic time complexity, making it prohibitively expensive at scale. We present a comprehensive comparative analysis and economic evaluation of four algorithmic approaches—naive exact counting, heap-based top-k selection, Space-Saving, and Count-Min Sketch—under realistic production workloads processing 10 billion daily events. Our empirical results demonstrate$10.9 \times$performance improvements and$\mathbf{9 6 \%}$memory reduction compared to exact methods, translating to $3.26 million annual cost savings. We provide productionready implementations, statistical validation (95 % confidence intervals, Bonferroni correction), and practical deployment guidelines, enabling informed technology adoption decisions for highcardinality streaming workloads.