Question #1907
An online gaming platform needs to collect application log data for real-time monitoring of player activities. The platform encounters variable traffic loads depending on peak gaming hours. The company requires a scalable solution that can handle changing data volumes efficiently.
Which solution meets these requirements?
Use a data stream in Amazon Kinesis Data Streams in on-demand mode to capture the logs. Use AWS Lambda to process the data in real time.
Use Amazon Kinesis Data Firehose to capture the logs. Use AWS Glue to process the data in real time.
Use Amazon Kinesis Video Streams to capture the logs. Use AWS Glue to process the data in real time.
Use Amazon Managed Service for Apache Flink to capture the logs. Use AWS Lambda to process the data in real time.
Explanation
The correct answer is A. Amazon Kinesis Data Streams in on-demand mode provides automatic scaling to handle unpredictable traffic, which aligns with the requirement for variable gaming traffic. AWS Lambda processes data in real time as it streams, ensuring immediate monitoring of player activities.
Why other options are incorrect:
- B: Kinesis Data Firehose buffers data before delivery (not real-time), and AWS Glue is designed for batch ETL, not real-time processing.
- C: Kinesis Video Streams is intended for video data, not application logs.
- D: Amazon Managed Service for Apache Flink is for processing, not data capture. Lambda alone cannot ingest logs without a streaming source like Kinesis.
Key Points:
1. Use Kinesis Data Streams (on-demand) for scalable, real-time data ingestion.
2. AWS Lambda enables serverless, real-time processing.
3. Avoid services like Kinesis Video Streams or AWS Glue for non-relevant use cases.
Answer
The correct answer is: A