AWS Certified Solutions Architect - Associate
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A company runs several websites on AWS for its different brands. Each website generates tens of gigabytes of web traffic logs each day. A solutions architect needs to design a scalable solution to give the company's developers the ability to analyze traffic patterns across all the company's websites. This analysis by the developers will occur on demand once a week over the course of several months. The solution must support queries with standard SQL.
Which solution will meet these requirements MOST cost-effectively?
Other
Community
UAnonymous
A
Store the logs in Amazon S3. Use Amazon Athena for analysis.
B
Store the logs in Amazon RDS. Use a database client for analysis.
C
Store the logs in Amazon OpenSearch Service. Use OpenSearch Service for analysis.
D
Store the logs in an Amazon EMR cluster. Use a supported open-source framework for SQL-based analysis.
Explanation:
Explanation
Correct Answer: A
Why Option A is correct:
- Cost-effectiveness: Amazon S3 provides highly durable and low-cost storage for large volumes of logs. Amazon Athena uses a serverless, pay-per-query model where you only pay for the data scanned during queries. Since analysis occurs only once a week, this pay-per-use model is highly cost-effective compared to running persistent infrastructure.
- Scalability: S3 can handle petabytes of data without provisioning, and Athena automatically scales to handle large datasets and complex queries.
- SQL Support: Athena uses standard ANSI SQL, meeting the requirement for SQL-based analysis.
- On-demand analysis: Perfect for weekly analysis patterns where infrastructure doesn't need to run continuously.
Why other options are less cost-effective:
Option B (Amazon RDS):
- RDS requires running database instances 24/7, incurring continuous costs even when not in use (weekly analysis only).
- Less suitable for large volumes of log data (tens of GB daily) compared to S3.
- Requires database maintenance, scaling, and backup management.
Option C (Amazon OpenSearch Service):
- Requires running OpenSearch clusters continuously, incurring ongoing compute and storage costs.
- While good for real-time analytics, it's overkill and expensive for weekly analysis.
- Uses its own query language (OpenSearch Query DSL), not standard SQL.
Option D (Amazon EMR cluster):
- Requires provisioning and managing EMR clusters, which would need to be running or started/stopped for analysis.
- More complex to manage and typically more expensive for infrequent analysis.
- Better suited for complex data processing pipelines rather than simple SQL queries on stored logs.
Key AWS Services Considered:
- Amazon S3: Low-cost, durable object storage ideal for log storage
- Amazon Athena: Serverless interactive query service that uses standard SQL to analyze data in S3
- AWS Glue Data Catalog: Can be used to create and manage table schemas for the log data
Cost Optimization Strategy: The solution leverages AWS's serverless and pay-per-use services, eliminating the need for running infrastructure continuously. Data is stored in low-cost S3, and queries are executed only when needed, making this the most cost-effective approach for weekly analysis patterns.
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