AWS Certified Solutions Architect Associate SAA-C03 Practice Question
A company is building a centralized logging solution that will aggregate logs from various microservices. Each microservice will publish log messages to a central system, which will then store the logs for analysis and monitoring purposes. Given the need for high throughput and the ability to handle spikes in log data volume without losing messages, which service should the Architect recommend for collecting log data?
Amazon Relational Database Service (RDS) Read Replicas
The correct answer is Amazon Kinesis Data Firehose (now called Amazon Data Firehose). Firehose is a fully managed streaming-ingestion service that reliably captures, buffers, and batches high-volume log data and then delivers it to durable storage such as Amazon S3, Amazon OpenSearch Service, or Amazon Redshift. It automatically provisions and scales resources to match incoming throughput, so sudden spikes in log traffic are absorbed without data loss.
Why the other choices are incorrect:
Amazon Simple Queue Service (SQS) can also scale to very high message rates, but it is a messaging queue, not a streaming ingestion and delivery service. You would still have to build consumer applications to read from the queue, batch, transform, and store the logs, adding operational overhead.
Amazon RDS Read Replicas provide read-only copies of relational databases and have no capability for streaming ingestion of log data.
Amazon S3 Glacier (S3 Glacier storage class) is a low-cost archival storage tier intended for long-term cold data, and it cannot accept real-time streaming writes.
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What is Amazon Kinesis Data Firehose?
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How does Kinesis Data Firehose handle high-volume data?
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Why shouldn't I use Amazon SQS for this logging solution?