HOTSPOT For each of the following statements, select Yes if the statement is tine. Otherwise, select No. NOTE: Each correct selection is worth one point.

Stream processing has access to the most recent data received or data within a rolling time window. ( Yes ) This is a fundamental characteristic of stream processing. It is designed to process unbounded streams of data in near real-time. To manage this continuous flow, computations are often performed over "windows" of time, such as a 5-minute rolling (or sliding) window, which provides access to the most recent events for analysis.

Batch processing must occur immediately and have latency in the order of seconds or milliseconds. ( No ) This statement describes stream processing, not batch processing. Batch processing is characterized by high latency, as it collects and processes large, finite volumes of data over a period (e.g., hours or days). In contrast, stream processing aims for low latency (milliseconds to seconds) to provide immediate insights.

Stream processing is used for simple response functions, aggregates, or calculations such as rolling averages. ( Yes ) This is a primary use case for stream processing. It excels at performing continuous calculations on data in motion. Common operations include stateful aggregations (e.g., counting events over a time period), transformations, and calculating metrics like rolling averages, which are essential for real-time monitoring and analytics dashboards.

Microsoft Azure Documentation. (2024). Introduction to Azure Stream Analytics. Microsoft Learn. In the section "How does Stream Analytics work?", it states, "Azure Stream Analytics makes it easy to ingest, process, and analyze real-time streaming data... You can also perform calculations such as running averages over a time window."

Microsoft Azure Documentation. (2023). Windowing functions (Azure Stream Analytics). Microsoft Learn. This document details how stream processing uses various windows (Tumbling, Hopping, Sliding) to operate on temporal blocks of data, confirming that it works on data within a time window.

Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly Media. In Chapter 11, "Stream Processing," Section: "Comparing Batch and Stream Processing," Kleppmann contrasts the two paradigms, noting that batch processing operates on bounded datasets with high latency, while stream processing operates on unbounded data with low latency. He explicitly mentions rolling averages as a typical stream processing task.

Akidau, T., Chernyak, S., & Lax, R. (2018). Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing. O'Reilly Media. Chapter 2, "The What: Core Concepts," describes how stream processing systems are designed for low-latency, continuous computations and frequently use windowing to scope operations on an otherwise infinite data stream.