DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer is building an AWS Glue ETL job that reads from an Amazon DynamoDB table and writes to Amazon S3. The DynamoDB table has a large number of items, and the engineer needs to ensure the job reads the data efficiently without consuming too much provisioned throughput. Which method should the engineer use to read from DynamoDB?

⚠ Common exam trap

The trap here is assuming that using the native DynamoDB connector with high throughput is the best way, when it can actually degrade performance by consuming provisioned capacity.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use the DynamoDB export to S3 feature to export the table to S3, then read from S3 in the Glue job.

DynamoDB export to S3 allows you to export a full table snapshot to S3 without consuming read capacity, making it ideal for large-scale ETL reads. The Glue job can then read the exported data from S3 in parallel, achieving high throughput without impacting the DynamoDB table's performance.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use Amazon Kinesis Data Streams to stream changes from DynamoDB to S3, then read from S3.
Why it's wrong here
Kinesis Data Streams can capture DynamoDB changes via DynamoDB Streams, but it is designed for real-time processing, not bulk historical reads. Setting up a stream for a one-time ETL job adds unnecessary complexity and does not efficiently read existing data.
✗
Use AWS Glue's native DynamoDB connector with the read throughput parameter set to a high value.
Why it's wrong here
Setting read throughput too high can consume excessive provisioned capacity, impacting other applications. While the native connector is appropriate, the throughput must be carefully tuned, not simply set high. This approach risks throttling and performance issues for other workloads.
✗
Use AWS Glue's DynamoDB connector with the 'dynamodb.scan' option to scan the table.
Why it's wrong here
Scanning the entire table consumes significant read capacity and can cause throttling. While the connector supports scan, it is not efficient for large tables. The export to S3 method is preferred for bulk reads without impacting throughput.
✓
Use the DynamoDB export to S3 feature to export the table to S3, then read from S3 in the Glue job.
Why this is correct
DynamoDB export to S3 is a serverless, efficient way to export large tables without consuming read capacity. The export creates a consistent snapshot in S3, which Glue can then read in parallel. This method avoids impacting the table's provisioned throughput and is recommended for large-scale reads.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

DEA-C01 Data Ingestion and Transformation Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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