DEA-C01 Data Ingestion and Transformation Practice Question
A company wants to ingest data from multiple SaaS applications into Amazon S3 using a fully managed service that supports schema discovery and transformation. Which AWS service should they use?
⚠ Common exam trap
The trap is confusing AppFlow with AWS Glue, as both can move data, but AppFlow is specifically for SaaS integration with built-in connectors and transformations.
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
✓
Amazon AppFlow
Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3. It supports schema discovery and transformation, making it ideal for ingesting data from multiple SaaS apps without writing code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon Kinesis Data Firehose
Why it's wrong here
Kinesis Data Firehose delivers streaming data to S3 but performs no schema discovery and only limited Lambda-based transformation. It is tempting because it writes directly to S3, yet it would be correct only for high-volume streaming sources already structured, not SaaS ingestion requiring schema inference.
- ✓
Amazon AppFlow
Why this is correct
Amazon AppFlow is the fully managed ingestion service that connects SaaS sources such as Salesforce and SAP to Amazon S3, performing schema discovery and optional transformation during the flow. It satisfies the no-code, managed requirement without building custom extraction pipelines.
- ✗
AWS Glue
Why it's wrong here
AWS Glue performs ETL and schema discovery on data already in AWS storage; it lacks native managed connectors that ingest from SaaS applications such as Salesforce and Marketo into S3. It is tempting because Glue handles schema discovery and transformation, and it would be correct once the SaaS data has landed in S3.
- ✗
AWS Data Pipeline
Why it's wrong here
AWS Data Pipeline orchestrates scheduled data movement between AWS services and on-premises sources, but it provides no SaaS connectors with schema discovery or transformation. It is tempting for ETL scheduling, yet it would be correct only for coordinating existing compute jobs, not ingesting SaaS data with schema inference.
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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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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