DEA-C01 Data Store Management Practice Question

A company is building a data pipeline that ingests streaming data from IoT devices. The data must be stored in a durable, scalable, and cost-effective manner for batch processing. Which TWO AWS services should be used together?

⚠ Common exam trap

Watch out — candidates often confuse Amazon Kinesis Data Streams with Amazon Kinesis Data Firehose (which directly writes to S3) or mistakenly choose Amazon Redshift for storage, overlooking that S3 is the correct durable and cost-effective storage layer for raw streaming data before any warehousing.

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 Streams

Amazon Kinesis Data Streams (B) is the correct ingestion service for streaming IoT data because it provides a durable, scalable, and real-time data streaming platform that can capture and store data records for up to 365 days. Amazon S3 (E) is the correct storage service for batch processing because it offers virtually unlimited durability (99.999999999%), cost-effective tiered storage, and native integration with batch processing frameworks like Amazon EMR and AWS Glue. Together, they form a classic streaming-to-batch pipeline: Kinesis ingests and buffers the streaming data, which is then persisted in S3 for downstream batch analytics.

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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Amazon ElastiCache
Why it's wrong here
ElastiCache is an in-memory cache for sub-millisecond reads, not durable storage; its data can be evicted or lost, so it cannot hold IoT streams reliably. It is tempting because it scales and reduces latency, but durability and cost-effective retention are absent.
✓
Amazon Kinesis Data Streams
Why this is correct
Amazon Kinesis Data Streams ingests the IoT telemetry durably and scales by shard, satisfying the streaming ingestion requirement. It decouples producers from consumers, buffering records for the companion storage service that handles batch processing, so ingestion spikes never overwhelm downstream analytics.
✗
Amazon Redshift
Why it's wrong here
Redshift is a data warehouse for analytical queries, not a durable ingestion buffer for streaming IoT data awaiting batch processing. It is tempting because batch processing often ends in a warehouse, but Redshift cannot absorb the ingestion stage that the pipeline requires.
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Amazon DynamoDB
Why it's wrong here
DynamoDB is a low-latency key-value store for transactional workloads, not a cost-effective durable landing zone for high-volume streaming data awaiting batch processing. It is tempting because it scales elastically, but per-request pricing and item-size limits make bulk retention expensive.
✓
Amazon S3
Why this is correct
Amazon S3 provides durable, highly scalable object storage with tiered pricing, satisfying the cost-effective, long-term storage constraint for batch processing. It decouples ingestion from processing, letting streaming data land cheaply before batch jobs read it, rather than paying for continuous compute or a database.

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 Store Management Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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