How to Calculate Daily DynamoDB Metrics Without Affecting Provisioned Capacity?

SAA-C03 By islever Editorial Last updated

Determine high-performing data ingestion and transformation solutions.

Short answer

B Use an AWS Glue job with the AWS Glue DynamoDB export connector to process an exported S3 snapshot for daily metrics without consuming provisioned RCUs.

Question

A company uses an Amazon DynamoDB table to store data that the company receives from devices. The DynamoDB table supports a customer-facing website to display recent activity on customer devices. The company configured the table with provisioned throughput for writes and reads. The company wants to calculate performance metrics for customer device data on a daily basis. The solution must have minimal effect on the table's provisioned read and write capacity. Which solution will meet these requirements?
  1. Use an Amazon Athena SQL query with the Amazon Athena DynamoDB connector to calculate performance metrics on a recurring schedule.
  2. Use an AWS Glue job with the AWS Glue DynamoDB export connector to calculate performance metrics on a recurring schedule. Correct Answer
  3. Use an Amazon Redshift COPY command to calculate performance metrics on a recurring schedule.
  4. Use an Amazon EMR job with an Apache Hive external table to calculate performance metrics on a recurring schedule.

Key Takeaways

Tested is minimizing impact on DynamoDB provisioned throughput during daily metric calculation, and the trap is assuming any query service can read a live DynamoDB table without consuming RCUs.

This SAA-C03 question tests how to run recurring analytics on a DynamoDB table without consuming provisioned read or write capacity. The page establishes that the AWS Glue DynamoDB export connector (B) is correct because it processes an exported S3 snapshot, whereas Athena, Redshift COPY, and EMR Hive all hit the live table.

Common mistake: Many learners choose A, the Athena DynamoDB connector, because it offers familiar SQL; however, it queries the live DynamoDB table directly and consumes provisioned read capacity, which violates the minimal-impact requirement.

Expert Analysis

Why the Answer Is Correct

Option B uses the AWS Glue DynamoDB export connector, which exports a point-in-time-recovery snapshot of the table to Amazon S3 and then lets Glue process that S3 data on a recurring schedule. DynamoDB's export-to-S3 feature does not consume provisioned read capacity, so the customer-facing website continues to read and write without capacity contention. The Glue job can calculate the daily performance metrics from the exported snapshot entirely outside the live table. As toyaji explained, the connector "exports table snapshot to s3 as dynamoDB-json object, then process on it." Commenter ksdpmx similarly noted that the export uses PITR "instead of DynamoDB directly," which is precisely why B meets the minimal-effect requirement.

Why the Other Options Are Wrong

Option A is wrong because the Amazon Athena DynamoDB connector runs SQL queries directly against the live DynamoDB table, consuming read capacity units for every scan. Option C is wrong because an Amazon Redshift COPY command reads from the source DynamoDB table and consumes provisioned read capacity, and it also requires additional data movement to calculate metrics. Option D is wrong because an Amazon EMR job with an Apache Hive external table maps to and scans the live DynamoDB table, again consuming RCUs. As LeonSauveterre summarized, B minimizes direct read/write operations on the table because the exported data is processed separately in S3, while A does not guarantee minimal impact. None of these alternatives can avoid touching the provisioned table unless data is first exported to S3.

Community Comment Notes

The comment consensus is overwhelmingly for B, with learners such as JoeTromundo and Anyio stressing that exporting to S3 avoids consuming the table's provisioned capacity. GOTJ made the key point that "Option 'B' is the only one that interacts with an EXPORTED COPY of the table," and then questioned whether the export itself consumes capacity, concluding from AWS documentation that it does not. Bogey echoed that the Glue DynamoDB export connector "doesn't consume read capacity units of the source DynamoDB tables," while spoved shared the official AWS Glue DynamoDB ELT connector blog post. A minority, including tonybuivannghia and AbhiBK, argued for A because Athena can query DynamoDB directly, but direct access is exactly the performance risk the question asks to avoid.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

When you see "minimal effect on the table's provisioned read and write capacity," immediately eliminate any option that queries or scans the live DynamoDB table. Look for a snapshot or export-to-S3 path, and remember that the AWS Glue DynamoDB export connector is the standard SAA-C03 answer for this pattern.

Frequently Asked Questions

Why does the Athena DynamoDB connector affect provisioned capacity?

It queries the live DynamoDB table directly, so each SQL scan consumes provisioned read capacity units and can slow the customer-facing website.

Does the AWS Glue DynamoDB export connector consume RCUs?

No. It exports a point-in-time-recovery snapshot to Amazon S3, and AWS documents that this export process does not consume the source table's provisioned read capacity.

More SAA-C03 FAQ →

Community Votes

B
100%

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

Discussion 14 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 14

  1. toyaji 👍 6 Answered B
    DynamoDB export connector literally "exports" table snapshot to s3 as dynamoDB-json object, then process on it. So it does not affect on read / write capacity on dynamoDB itself. But Athena query directly on dynamoDB so affects on read / write capacity
  2. LeonSauveterre 👍 3 Answered B
    A - Athena allows you to use standard SQL, but it doesn't guarantee minimal impact on the table. B - It minimizes direct read/write operations on the table because the exported data can be processed separately (for example, export to S3 then do the processing). C - Cool but there's no native connector to directly pull data from DynamoDB. You must export the data to S3 or another source first, adding complexity. D - EMR impacts throughput significantly.
  3. JoeTromundo 👍 3 Answered B
    The DynamoDB export connector allows you to export data from DynamoDB to other storage solutions like Amazon S3 without consuming the table's provisioned read capacity, ensuring minimal impact on the performance of the table.
  4. spoved 👍 3 Answered B
    https://aws.amazon.com/blogs/big-data/accelerate-amazon-dynamodb-data-access-in-aws-glue-jobs-using-the-new-aws-glue-dynamodb-elt-connector/
  5. GOTJ 👍 2 Answered B
    The trick here is to avoid, by any means, interact with the original DynamoDB table because doing so would affect the provisioned capacity. Option "B" is the only one that interacts with an EXPORTED COPY of the table. So, the question is: would provisioned capacity be affected by the DynamoDB export process? The answer is: NO! (https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-connect-dynamodb-home.html): "DynamoDB export to S3 allows you to export both full and incremental data from your DynamoDB table. Exports do not consume any read capacity units (RCUs) and have no impact on table performance and availability"
Show 9 more comments
  1. AbhiBK 👍 2
    To calculate performance metrics for customer device data on a daily basis with minimal effect on the table’s provisioned read and write capacity, the best solution would be: A. Use an Amazon Athena SQL query with the Amazon Athena DynamoDB connector to calculate performance metrics on a recurring schedule. This approach allows you to run SQL queries directly on the data stored in DynamoDB without impacting the provisioned throughput, as Athena queries are serverless and do not consume DynamoDB read or write capacity1.
  2. Anyio 👍 2 Answered B
    The correct answer is B. Use an AWS Glue job with the AWS Glue DynamoDB export connector to calculate performance metrics on a recurring schedule. Explanation: Option B: Correct. AWS Glue offers an efficient method to extract, transform, and load (ETL) data from DynamoDB to Amazon S3, without affecting the table’s provisioned throughput as significantly. Once data is in S3, it can be further processed or queried using AWS Glue or other analytics services, without impacting DynamoDB's immediate R/W provisioning.
  3. Bogey 👍 2
    B. Instead, the new AWS Glue DynamoDB export connector reads DynamoDB data from the snapshot, which is exported from DynamoDB tables. This approach has following benefits: It doesn’t consume read capacity units of the source DynamoDB tables
  4. tonybuivannghia 👍 2 Answered A
    I think A is correct. We can query the data from DynamoDB by Amazon Athena DynamoDB connector directly, not via S3 Bucket.
  5. ksdpmx 👍 2
    why is B wrong.. Glue DynamoDB export connector will read data from PITR instead of DynamoDB directly..
  6. rockyykrish 👍 1 Answered A
    Explanation: Amazon Athena with DynamoDB Connector: Athena is a serverless interactive query service that allows you to run SQL queries directly on data in various storage systems, including DynamoDB, through the Athena DynamoDB connector. This setup minimizes the impact on the provisioned read and write capacity of the DynamoDB table because the connector reads data efficiently without directly querying the table. Performance Metrics Calculation: Athena provides SQL capabilities to compute performance metrics on the data fetched via the DynamoDB connector. The queries can be scheduled using Amazon EventBridge or other automation tools to run daily. Minimal Operational Overhead: Athena requires no infrastructure to manage, making it a low-maintenance solution. It is cost-effective since you pay only for the queries you run.
  7. RealPro111 👍 3 Answered B
    The right answer is B
  8. dhewa 👍 2
    I go with A
  9. siheom 👍 2
    VOTE B

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