Real-Time Network Outage Detection with Kinesis and Flink
A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs an application to process the usage data in real time. The company aggregates and stores the data in an Amazon Aurora DB instance. Sudden drops in network usage usually indicate a network outage. The company must be able to identify sudden drops in network usage so the company can take immediate remedial actions. Which solution will meet this requirement with the LEAST latency?
Community Votes
77% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The question tests real-time stream processing; the common trap is confusing database activity streams (auditing) with application data analytics, leading candidates to choose Option D.
This page analyzes the optimal architecture for detecting sudden network usage drops with the least latency using Amazon Kinesis Data Analytics (Flink). It establishes that streaming analytics is superior to polling or database auditing for real-time event detection.
Many learners select Option D because they assume 'Database Activity Streams' implies real-time monitoring of data changes, but it is designed for security auditing, not business logic analysis.
Community Discussion (8 comments)
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Expert Analysis
Why the Answer Is Correct
Option B is the correct answer because Amazon Kinesis Data Streams combined with Amazon Managed Service for Apache Flink provides a true real-time processing pipeline. The application publishes data to Kinesis, and Flink continuously processes the stream, allowing for immediate detection of anomalies like sudden usage drops without the latency introduced by periodic polling or batch storage.Why the Other Options Are Wrong
Option A introduces unnecessary latency by querying Aurora every minute via EventBridge, which fails the 'least latency' requirement. Option C replaces Aurora with DynamoDB but still relies on Lambda polling every minute, which is not real-time. Option D leverages Database Activity Streams, which capture SQL statements for audit compliance, not application-level metrics, making it unsuitable for detecting network usage patterns.Community Comment Notes
The community largely agrees with Option B, citing its efficiency in analyzing data before it hits the database. Some users initially considered Option D, believing it offered lower latency, but were corrected that it serves an auditing purpose rather than analytical needs. One commenter noted that Option B 'reduces latency because it is analyze before the data even gets to the Aurora DB,' highlighting the architectural advantage of stream processing.Official Reference
Exam Strategy
Always distinguish between 'streaming data' (Kinesis/Flink) and 'database activity' (Audit Logs). When 'least latency' and 'real-time' are keywords, look for continuous stream processing solutions rather than scheduled polls or historical queries.
Frequently Asked Questions
Why isn't Aurora Database Activity Streams suitable for this?
Activity Streams capture SQL operations for security auditing, not business metrics like network usage volumes.
Does Lambda polling meet the least latency requirement?
No, polling every minute introduces significant delay compared to the near-instant processing of a Flink stream.
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