How to Join DynamoDB, RDS, Redshift, and S3 Cost-Effectively?
Short answer
C Use Amazon Athena Federated Query to join DynamoDB, RDS, Redshift, and S3 data in place without copying it for the one-time analysis.
Question
Key Takeaways
The question tests choosing a serverless, no-copy query approach for a one-time multi-source join; the common trap is assuming Athena only queries S3 and therefore copying all data into S3 first.
Amazon Athena Federated Query lets a single SQL query join data across DynamoDB, RDS, Redshift, and S3 without moving or duplicating it, making it the most cost-effective option for a one-time analysis. This page explains why option C beats ETL-to-S3, provisioned EMR, and Redshift Spectrum for this scenario.
Common mistake: The most common wrong choice is B: copying DynamoDB, RDS, and Redshift data into S3 before querying with Athena. That adds storage, transfer, and ETL overhead, which is unnecessary for a one-time job when Athena Federated Query can read the sources in place.
Expert Analysis
Why the Answer Is Correct
Amazon Athena Federated Query is designed for querying data where it already lives: DynamoDB, RDS via JDBC, Redshift, and S3 can be combined in Athena SQL, with S3 queried natively and the other sources reached through connectors. Because Athena is serverless and charges per query, there is no cluster to provision or pay for between runs, which fits a one-time analysis job. The approach avoids copying or duplicating data into S3, eliminating storage, transfer, and ETL effort before the join. That combination of no data movement and pay-per-query execution makes option C the most cost-effective fit.
Why the Other Options Are Wrong
A uses an EMR provisioned cluster and Apache Spark; it can join the sources, but you pay for cluster capacity and operational overhead even for a single run. B requires extracting DynamoDB, RDS, and Redshift data into S3 first, which adds storage, transfer, and pipeline work before Athena can query it. D uses Redshift Spectrum, which is designed primarily for querying S3 external tables from a Redshift cluster; it does not natively query DynamoDB and RDS directly and still requires Redshift compute. For a one-time, cost-sensitive join, those extra resources and data copies make A, B, and D less attractive than C.
Community Comment Notes
lucas_rfsb voted for C and noted that Federated Query is typical for this purpose because it avoids adding or duplicating resources in S3, while also acknowledging that centralizing data in S3 can make governance easier. pypelyncar highlighted "Serverless Processing" and pay-per-query execution as the reason Athena suits one-time jobs. Another commenter pointed to the AWS Athena connectors list as documentation that these sources can be queried natively. certplan raised a useful cost caveat: S3 storage is cheap and Athena is optimized for S3, so repeatable or governance-heavy workloads may still favor copying data, but that does not change the one-time, no-copy answer here.
Official Reference
https://docs.aws.amazon.com/athena/latest/ug/connectors-available.html
This link opens the official vendor documentation in a new tab.
Exam Strategy
For one-time, multi-source analytics, look for serverless query services that can read sources in place; avoid options that provision clusters or duplicate data. The phrase 'one-time analysis' is a strong signal for Athena Federated Query over ETL-to-S3 or EMR.
Frequently Asked Questions
Why is copying all data into Amazon S3 before querying not the most cost-effective choice?
Can Redshift Spectrum query DynamoDB and RDS directly?
Community Votes
100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Discussion 6 contributions
Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.
From public community threads 6
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lucas_rfsb 👍 7 Answered CI would go for C because Federated Query is typical for this porpouse. Besides, we don't need to add/duplicate resources in S3. But I see that, becasuse Athena is more optimized for S3, it can be considered a tricky question, since there can be more trade-offs to consider, such as data governance that are easier if data is centralized in S3 in my opinion.
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pypelyncar 👍 4 Answered CServerless Processing: Athena is a serverless query service, meaning you only pay for the queries you run. This eliminates the need to provision and manage compute resources like in EMR clusters, making it ideal for one-time jobs. Federated Query Capability: Athena Federated Query allows you to directly query data from various sources like DynamoDB, RDS, Redshift, and S3 without physically moving the data. This eliminates data movement costs and simplifies the analysis process. Reduced Cost for Large Datasets: Compared to copying data to S3, which can be expensive for large datasets, Athena Federated Query avoids unnecessary data movement, reducing overall costs.
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[Removed] 👍 4 Answered CYou can query these sources by using Federated Queries, which is a native feature of Athena. The other options may increase costs and operational overhead, as they use more than one service to achieve the same result https://docs.aws.amazon.com/athena/latest/ug/connectors-available.html
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certplan 👍 2Amazon Athena Federated Query allows you to query data from multiple federated data sources including relational databases, NoSQL databases, and object stores directly from Athena. While this might seem like an efficient way to join data from different sources without the need for copying data into Amazon S3, it's essential to consider the cost implications. AWS documentation on Amazon Athena Federated Query [1] explains that while Federated Query enables you to query data from external data sources without data movement, it does not eliminate data transfer costs. Depending on the data sources involved (such as Amazon RDS, DynamoDB, etc.), there might be data transfer costs associated with querying data directly from these sources. [1] Amazon Athena Federated Query Documentation: https://docs.aws.amazon.com/athena/latest/ug/federated-data-sources.html
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certplan 👍 11. Data Storage Costs: Storing data in Amazon S3 is generally cheaper compared to the other AWS storage options like Amazon Redshift or Amazon RDS. 2. Compute Costs: Amazon: Athena is a serverless query service that allows you to query data directly from S3 without the need for provisioning or managing infrastructure. You only pay for the queries you run, which can be more cost-effective compared to provisioning an EMR cluster (option A) or using Redshift Spectrum (option D), both of which involve compute resources that you might not fully utilize. 3. Data Transfer Costs: Option B involves copying the data once into S3, and then there are no additional data transfer costs for querying the data using Athena. In contrast, options A and D would involve data transfer costs as data is moved between different services. Amazon Athena Pricing: https://aws.amazon.com/athena/pricing/ Amazon S3 Pricing: https://aws.amazon.com/s3/pricing/
Show 1 more comment
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certplan 👍 1Point: "perform a one-time analysis job" Option C (Amazon Athena Federated Query) might seem appealing, but it's generally more suited for querying data from external sources without copying the data into S3. However, since the data is already within AWS services, copying it to S3 and using Athena directly would likely be more cost-effective.
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