Unleashing the Power of Probabilistic Data Structures: Optimizing Storage and Performance for Big Data
Probabilistic data structures are a type of statistical algorithm designed to optimize the use of memory in storing and querying large datasets. These structures employ probabilistic algorithms to estimate the presence of elements in a dataset with a high degree of accuracy while minimizing the amount of memory required for storage. In this session, we will explore some fundamental analytical questions that, when answered accurately for very large datasets, require substantial resources and cost. This is particularly relevant in streaming data use cases such as real-time monitoring, fraud detection, social media analytics, and online advertising, where the timely availability of analytics takes precedence over their accuracy.