DEA-C01 Data Ingestion and Transformation Practice Question
A company is building a data lake on S3 and needs to ingest data from on-premises Oracle database. The data is 5 TB and changes incrementally. The ingestion must capture changes in near real-time (less than 1 minute latency) and be cost-effective. Which approach should be used?
⚠ Common exam trap
Candidates often confuse Kinesis Data Firehose's ability to accept data from custom sources with native JDBC support, leading them to choose Option B, but Firehose lacks built-in CDC connectors for relational databases like Oracle.
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 AWS Database Migration Service (DMS) with ongoing replication to S3
AWS DMS with ongoing replication captures incremental changes from Oracle using its native change data capture (CDC) mechanism, such as Oracle LogMiner or binary logs, and streams them to S3 in near real-time with latency under 1 minute. This approach is cost-effective because DMS charges only for the compute resources used during replication, and S3 storage is inexpensive, making it ideal for a 5 TB dataset with continuous changes.
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 AWS Database Migration Service (DMS) with ongoing replication to S3
Why this is correct
DMS supports CDC and can replicate changes to S3 with low latency.
- ✗
Use Amazon Kinesis Data Firehose with an Oracle JDBC connector
Why it's wrong here
Firehose does not natively support Oracle as a source; custom solution needed.
- ✗
Use AWS Glue to perform a full table export daily
Why it's wrong here
Full export does not provide near real-time CDC.
- ✗
Use AWS DataSync to sync the Oracle data files to S3
Why it's wrong here
DataSync is for file systems, not databases.
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
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