Huawei Cloud MapReduce service MRS provides real-time distributed computing cabinets to analyze and calculate massive real-time data streams. In the elevator networking industry, smart elevator data…

Question

Options

• AHue
• BHive
• CKafka
• DFlume

• A
  15% (6)
• B
  8% (3)
• C
  78% (31)

Explanation

Kafka and Flume are the correct choices because this scenario specifically requires real-time data ingestion from massive sources. Kafka is a distributed message-streaming platform built to handle high-throughput, fault-tolerant data pipelines from thousands of simultaneous producers - exactly what smart elevator IoT devices require for real-time monitoring. Flume complements Kafka by acting as a reliable data collection agent that gathers, aggregates, and moves continuous data streams (such as sensor events) into the cluster in real time.

Hive (B) is a SQL-on-Hadoop batch query engine used for offline analytical queries against stored data - it has no role in real-time data ingestion or stream processing. Hue (A) is a web-based GUI for browsing and querying Hadoop data; it is a user interface tool, not a data pipeline component, so it contributes nothing to collection or streaming.

Memory tip: For any real-time streaming scenario, look for the collector (Flume) and the stream broker (Kafka) - Hive and Hue belong to the "query and visualize after the fact" layer, not the ingestion layer.

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