Streaming Algorithms
Streaming algorithms are computational methods designed to process data streams where input arrives sequentially and cannot be stored entirely in memory due to size constraints. They focus on approximating statistics or properties of the data (like frequency counts, distinct elements, or quantiles) using limited memory and processing time. These algorithms are essential for real-time analytics in systems handling massive, continuous data flows.
Developers should learn streaming algorithms when building or optimizing systems that handle high-volume, real-time data, such as network monitoring, financial tickers, social media feeds, or IoT sensor streams. They are crucial for applications requiring immediate insights from data that cannot be fully stored, enabling efficient resource usage and scalability in big data environments.