A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs…

The core requirement is to analyze a high-velocity data stream (thousands of points per second) in real time to detect anomalies with the least latency. The combination of Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink is purpose-built for this use case. This architecture enables continuous, stateful processing of streaming data. An Apache Flink application can use time-based windows (e.g., sliding windows) to compute aggregates and compare them in real time, allowing for the immediate detection of sudden drops with sub-second latency, which is significantly faster than any polling-based approach.

1. Amazon Kinesis Data Analytics for Apache Flink Developer Guide: The guide explicitly states

"You can use Amazon Kinesis Data Analytics to process and analyze streaming data for a variety of use cases

" including "Time-series analytics: You want to get insights from your streaming data by computing metrics over a time window." This directly matches the need to detect drops in network usage over time.

Source: AWS Documentation

"Amazon Kinesis Data Analytics for Apache Flink Developer Guide

" What Is Amazon Kinesis Data Analytics?

Use Cases.

2. Amazon Kinesis Data Streams Developer Guide: This guide describes Kinesis Data Streams as a service for collecting and processing large streams of data records in real time. It highlights its suitability for "Real-time analytics" use cases

which is the central task in the question.

Source: AWS Documentation

"Amazon Kinesis Data Streams Developer Guide

" What Is Amazon Kinesis Data Streams?

Use Cases for Kinesis Data Streams.

3. Amazon EventBridge User Guide: The documentation on scheduling shows that the finest granularity for rate-based schedules is one minute (e.g.

rate(1 minute)). This confirms the inherent latency in options A and C

making them unsuitable for the requirement.

Source: AWS Documentation

"Amazon EventBridge User Guide

" Creating a rule that runs on a schedule

Schedule expressions for rules.

4. Amazon Aurora User Guide: The documentation for Database Activity Streams (DAS) states

"Use database activity streams to audit the database activity of your DB clusters... DAS provides a near-real-time stream of the activity in your database to help you do this." This clarifies its purpose is for auditing database access and activity

not analyzing the application data itself.

Source: AWS Documentation

"Amazon Aurora User Guide

" Using Database Activity Streams with Aurora.