Question #1380
A social media platform requires real-time interaction data to be accessed with sub-millisecond latency and occasional analysis on historical datasets. Which solution meets these requirements with the LEAST operational overhead?
Use Amazon Aurora for frequently accessed data. Schedule periodic exports to an Amazon S3 bucket using AWS Lambda. Run one-time queries on the S3 data using Amazon Redshift.
Store all data directly in Amazon S3. Use S3 Lifecycle policies to archive older data to S3 Glacier Flexible Retrieval. Perform one-time queries using Amazon Athena.
Use Amazon DynamoDB with DynamoDB Accelerator (DAX) for real-time data access. Export data to Amazon S3 via DynamoDB table exports. Analyze historical data in S3 using Amazon Athena.
Use Amazon DynamoDB for real-time data. Enable Kinesis Data Streams for continuous data capture. Use Kinesis Data Firehose to buffer and store data in Amazon S3.
Explanation
The correct answer is C because:
- DynamoDB with DAX provides sub-millisecond latency for real-time data access, meeting the primary requirement.
- DynamoDB exports to S3 are automated and require minimal setup, reducing operational effort.
- Amazon Athena allows serverless SQL queries on S3 data, eliminating infrastructure management for historical analysis.
Other options fail because:
- A: Aurora may not achieve sub-millisecond latency, and Lambda exports add operational steps.
- B: S3 lacks real-time access capabilities.
- D: Kinesis introduces complexity compared to DynamoDB's native S3 exports.
Key Points:
1. DynamoDB + DAX = low-latency real-time access.
2. DynamoDB exports to S3 are low-effort.
3. Athena enables serverless historical analysis.
Answer
The correct answer is: C