DEA-C01 practice question 16 of 25

A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. Which solution will meet this requirement MOST cost-effectively?

Full set: 287 questions
Answer options
A: Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
B: Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
C: Use Amazon Athena Federated Query to join the data from all data sources. Correct answer
D: Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.
Correct Answer:
C. Use Amazon Athena Federated Query to join the data from all data sources.
Explanation

Amazon Athena Federated Query is the most cost-effective solution for a one-time analysis job across multiple data sources. It allows you to run SQL queries across data stored in relational, non-relational, object, and custom data sources without moving the data. Since Athena is serverless, you pay only for the queries you run, avoiding the costs associated with provisioning and managing infrastructure like an EMR or Redshift cluster. This is ideal for infrequent, ad-hoc analysis, minimizing both operational overhead and cost.

Why Incorrect

A. Provisioning an Amazon EMR cluster is not cost-effective for a one-time job due to instance costs, even for a short duration, and the overhead of cluster setup.
B. This option requires building and running ETL jobs to move data into S3, which adds complexity, latency, and cost for a one-time query.
D. Using Redshift Spectrum requires a running Redshift cluster, which incurs continuous costs, making it more expensive than serverless Athena for a single analysis task.

References

1. AWS Documentation. (2023). Using Amazon Athena Federated Query. Amazon Athena User Guide. Retrieved from https://docs.aws.amazon.com/athena/latest/ug/what-is-federated-query.html (See the "Overview" section which states, "you can run SQL queries across data stored in relational, non-relational, object, and custom data sources.")

2. AWS Documentation. (2023). Amazon Athena Pricing. Retrieved from https://aws.amazon.com/athena/pricing/ (The pricing model is "pay per query," which is ideal for one-time jobs.)

3. AWS Documentation. (2023). Considerations and limitations for federated queries. Amazon Redshift Database Developer Guide. Retrieved from https://docs.aws.amazon.com/redshift/latest/dg/federated-query-considerations.html (This shows Redshift can do it, but it requires a provisioned cluster, making it less cost-effective than Athena for this scenario.)

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