DEA-C01 Data Ingestion and Transformation Practice Question

A company wants to ingest real-time data from a social media API into Amazon S3 for analysis. The API provides data as JSON records. Which AWS service is best suited for this ingestion?

⚠ Common exam trap

Watch out — candidates often confuse Amazon Kinesis Data Streams (which requires custom consumers) with Kinesis Data Firehose (which is serverless and directly writes to S3), or they incorrectly assume SQS can directly deliver to S3 without additional processing.

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 Kinesis Data Firehose

Amazon Kinesis Data Firehose is the best choice because it is a fully managed service designed to ingest real-time streaming data, such as JSON records from a social media API, and automatically load it into Amazon S3 with optional data transformation and compression. It handles scaling, buffering, and delivery without requiring custom code or infrastructure management, making it ideal for this use case.

Answer analysis

Option-by-option breakdown

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

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AWS Glue
Why it's wrong here
AWS Glue is a batch-oriented ETL service that runs on schedules or triggers, so it cannot continuously ingest streaming JSON records from a social media API. It is tempting because Glue excels at transforming and cataloguing JSON data already landed in Amazon S3, which would be the right choice for scheduled batch transformation jobs rather than real-time ingestion.
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Amazon Kinesis Data Firehose
Why this is correct
Amazon Kinesis Data Firehose is a fully managed delivery service that ingests streaming JSON records and loads them directly into Amazon S3, handling buffering, batching and scaling. It satisfies the real-time ingestion-to-S3 requirement without custom consumers, unlike Kinesis Data Streams, which requires separate delivery logic.
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Amazon Simple Queue Service (SQS)
Why it's wrong here
Amazon SQS decouples producers from consumers by storing messages in a queue, but it does not write JSON records into Amazon S3, so it cannot satisfy the ingestion target. It is tempting because SQS genuinely buffers streaming events for downstream pollers, and would be correct when you need durable, asynchronous message queuing between distributed components rather than direct delivery into S3.
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Amazon DataZone
Why it's wrong here
Amazon DataZone is a data governance and cataloguing service, not an ingestion pipeline; it cannot call the social media API or write JSON records into Amazon S3. It is tempting because it manages data assets and lineage across accounts, which would be the right choice for cataloguing and sharing datasets already landed in S3.

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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