DEA-C01 Data Ingestion and Transformation Practice Question
A marketing analytics team needs to ingest customer transaction data from an on-premises PostgreSQL database into Amazon S3 for analysis. The data volume is about 10 GB daily, and the team wants to perform full refresh daily (truncate and load) into S3 as Parquet files. The company has a Direct Connect connection to AWS. The team needs a simple, managed solution that minimizes operational overhead. What should the team use?
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 an AWS Glue ETL job with a JDBC connection to the PostgreSQL database, extract data, and write to S3 in Parquet format.
AWS Glue ETL job is the best choice for this scenario. It is a fully managed service that can connect to on-premises PostgreSQL via a JDBC connection (using an AWS Glue connection with the appropriate network configuration over Direct Connect). It can perform a full extract (truncate and load) each day and write the data directly to S3 in Parquet format, minimizing operational overhead. Option A (AWS DMS) is designed for ongoing change data capture (CDC) and continuous replication, not for daily full refreshes without incremental changes. Option B (Amazon EMR with Spark) requires managing clusters and is more complex than necessary for a simple daily load. Option D (AWS Data Pipeline) requires custom scripting and more configuration compared to Glue's built-in ETL capabilities. Therefore, option C is correct.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set up AWS Database Migration Service (DMS) to continuously replicate data to S3 in Parquet format.
Why it's wrong here
DMS is for ongoing replication, not optimized for periodic full refresh; it may introduce unnecessary overhead.
- ✗
Use Amazon EMR with a Spark job that reads from PostgreSQL and writes to S3.
Why it's wrong here
Configuring and managing EMR clusters introduces significant operational overhead for a 10 GB daily workload, failing the requirement for a managed solution. While Spark on EMR is ideal for complex large-scale data transformations or massive datasets requiring distributed processing power, it requires manual cluster tuning and lifecycle management. In this scenario, AWS Glue provides the necessary serverless orchestration to automate the ETL process without managing underlying infrastructure.
- ✓
Use an AWS Glue ETL job with a JDBC connection to the PostgreSQL database, extract data, and write to S3 in Parquet format.
Why this is correct
Glue is serverless and can handle daily full refresh with minimal setup.
- ✗
Use AWS Data Pipeline with a SQLActivity to extract data and copy to S3.
Why it's wrong here
Data Pipeline is older and less managed than Glue; requires more manual setup.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.