How to Join DynamoDB, RDS, Redshift, and S3 Cost-Effectively?

Analyze data by using AWS services.

Answer Correct 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.

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?

Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
Use Amazon Athena Federated Query to join the data from all data sources. Correct Answer
Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

Community Votes

100%

100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

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.

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.

Community Discussion (6 comments)

lucas_rfsb 👍 7 Selected: C

I 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.

pypelyncar 👍 4 Selected: C

Serverless 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.

certplan 👍 2

Amazon 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

certplan 👍 1

1. 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/

certplan 👍 1

Point: "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.

[Removed] 👍 4 Selected: C

You 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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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

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?

Copying adds storage, data transfer, and ETL time before the one-time query, while Athena Federated Query can read DynamoDB, RDS, Redshift, and S3 in place.

Can Redshift Spectrum query DynamoDB and RDS directly?

No, Redshift Spectrum is primarily for querying S3 external tables from Redshift; it does not natively join DynamoDB and RDS without additional loading or connectors.

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