Question 1,752 of 1,711
Change Data Capture from RDS to S3 Using AWS Glue or DMS
A data engineer is ingesting data from an Amazon RDS for PostgreSQL database into Amazon S3 using AWS Glue. The Glue job reads the entire table each time it runs, which takes several hours. The team wants to reduce the job duration by reading only new or updated records. Which approach should the engineer adopt?
Quick Answer
The correct approach is to enable job bookmarks in AWS Glue and use a timestamp column as the bookmark key to read only incremental data, effectively implementing change data capture from RDS to S3. This works because Glue job bookmarks track previously processed rows based on a designated column—such as last_updated—so subsequent runs skip unchanged records, drastically reducing job duration. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of incremental processing patterns versus full table scans; a common trap is choosing to increase workers or add partitioning, which still reads the entire dataset. Remember that CDC via Glue bookmarks or AWS DMS is purpose-built for efficient, periodic ingestion of changed records from RDS to S3. Memory tip: “Bookmark the timestamp, skip the past.”
⚠ Common exam trap
A common mix-up: candidates confuse increasing parallelism (Option C) with reducing data volume, or assume that source-side partitioning (Option B) automatically translates to incremental reads in Glue, when in fact Glue job bookmarks are the native mechanism for incremental processing in batch jobs.
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
✓
Enable job bookmarks in AWS Glue and use a column with timestamps as the bookmark key to read only incremental data.
AWS Glue job bookmarks track previously processed data using a specified column (e.g., a timestamp column) as the bookmark key. When enabled, the job reads only new or updated records since the last run, significantly reducing job duration by avoiding full table scans. This directly addresses the requirement to read incremental data from the PostgreSQL source.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable job bookmarks in AWS Glue and use a column with timestamps as the bookmark key to read only incremental data.
Why this is correct
Glue bookmarks track processed records; using a timestamp column allows incremental reads.
- ✗
Partition the table in the source database by date and read only the latest partition.
Why it's wrong here
Partitioning helps but still reads the entire partition; not incremental if updates exist.
- ✗
Increase the number of Glue workers to improve parallel reads.
Why it's wrong here
Speeds up full reads but does not reduce data volume; still reads entire table.
- ✗
Use Amazon Kinesis Data Streams to capture changes from PostgreSQL.
Why it's wrong here
Kinesis requires additional setup (e.g., Kinesis adapter) and is for real-time, not batch.
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 |
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Same concept, more angles
2 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer needs to ingest data from an Amazon RDS MySQL database into a data lake on Amazon S3. The engineer wants to perform an initial full load and then capture incremental changes. Which TWO AWS services can be combined to achieve this?
easy- A.Amazon Kinesis Data Firehose
- B.AWS Glue
- ✓ C.Amazon S3
- ✓ D.AWS Database Migration Service (DMS)
- E.AWS Data Pipeline
Why C: Amazon S3 is the target data lake storage layer, not a service that performs data ingestion or change data capture. However, it is listed as a correct option because the question asks which services can be combined to achieve the goal, and S3 is the essential destination for the data lake. The actual ingestion and CDC are handled by AWS DMS, which writes full load and incremental changes directly to S3 in formats like Parquet or CSV.
Variation 2. A data engineer needs to ingest data from an Amazon RDS for MySQL database into Amazon S3 on a daily basis. The data volume is about 50 GB per day. The engineer wants to minimize the impact on the source database. Which AWS service should be used?
medium- A.AWS Glue with a JDBC connection
- B.Amazon Athena Federated Query
- ✓ C.AWS Database Migration Service (DMS)
- D.AWS DataSync
Why C: AWS DMS can perform full load and ongoing replication with minimal impact on the source database. Option A is wrong because AWS Glue with a JDBC connection can impact the source due to high query load. Option B is wrong because Amazon Athena Federated Query reads data directly from RDS, which can cause performance issues. Option D is wrong because AWS DataSync is designed for file storage, not databases.
Last reviewed: Jul 4, 2026
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