Which of the following scenarios is Flume suitable for data collection? (Multiple choice)

Question

Options

• ACollect data from data sources such as Thrift, Avro, Syslog, Kafka, etc.
• BLocal file data collection
• CLog collection generated by the application system
• DReal-time data collection of large volumes of data

• A
  69% (20)
• D
  31% (9)

Explanation

A, B, and C represent Flume's core design purposes. Flume was built specifically as a reliable log aggregation and data transport system, and it ships with native source connectors for Thrift, Avro, Syslog, and Kafka (A), a Spooling Directory/Exec source for local files (B), and application log collection is literally Flume's primary use case - it was created at Cloudera to solve exactly this problem (C).

Option D is incorrect because "real-time data collection of large volumes of data" describes Kafka's strength, not Flume's. Flume introduces latency through its agent-channel-sink pipeline architecture and is designed for reliable transport rather than low-latency real-time streaming; Kafka is the standard choice when true real-time throughput at scale is required.

Memory tip: Think of Flume as a log plumber - it pipes data from named sources (Thrift, Avro, files, app logs) into storage reliably. When you see "real-time + massive scale," think Kafka (the firehose), not Flume (the pipe).

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