👉 Snapshot math is a computational approach that captures and processes data in discrete, snapshot-like intervals rather than continuously. This method involves taking a snapshot of the current state of a system or dataset at specific points in time, which are then analyzed independently. This approach is particularly useful for real-time data analysis, machine learning models that require frequent updates, and scenarios where continuous computation is impractical due to resource constraints. By focusing on these snapshots, snapshot math enables efficient and scalable processing, making it ideal for applications like real-time analytics, predictive modeling, and data-driven decision-making in dynamic environments.
