DEA-C01 Data Store Management Practice Question

A data engineer needs to store a large volume of time-series data from IoT sensors. The data will be queried by timestamp and sensor ID, and the engineer wants to use a managed AWS database that can handle high write throughput and provide fast queries on recent data. The engineer also wants to automatically expire old data after 90 days to reduce storage costs. Which AWS service should the engineer use?

⚠ Common exam trap

The trap here is assuming that DynamoDB with TTL is sufficient for time-series data, but Timestream offers purpose-built time-series capabilities and automatic retention that simplify management.

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 Timestream

Amazon Timestream is a fully managed time-series database that handles high write throughput, provides fast queries on recent data, and supports automatic data retention policies. It is designed specifically for time-series data like IoT sensor readings, with built-in functions for time-based queries. Other options either lack native time-series optimization or require manual management of retention and querying.

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 DynamoDB with TTL
Why it's wrong here
DynamoDB can handle high write throughput and TTL can automatically delete expired items. However, querying time-series data by timestamp and sensor ID efficiently requires careful schema design and secondary indexes. While possible, DynamoDB is not purpose-built for time-series data, and the engineer would need to manage indexes and partition keys. Amazon Timestream is a better fit because it is designed for time-series data with built-in time-based functions and automatic data lifecycle management.
✓
Amazon Timestream
Why this is correct
Amazon Timestream is a purpose-built time-series database that automatically scales to handle high write throughput and provides fast queries on recent data. It has built-in time-series functions and supports automatic data retention policies to expire data after a specified period, such as 90 days. This directly matches the requirements for IoT sensor data with timestamp and sensor ID queries and automatic data expiration.
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Amazon RDS for PostgreSQL with table partitioning
Why it's wrong here
RDS for PostgreSQL can handle time-series data with partitioning, but it requires manual management of partitions and retention. It may not scale as seamlessly for high write throughput as Timestream. Also, automating data expiration would require custom scripts or jobs. Timestream provides these features natively, making it more suitable for this scenario.
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Amazon S3 with lifecycle policies
Why it's wrong here
S3 is not a database and does not provide query capabilities for time-series data without additional services like Athena. While lifecycle policies can expire data, querying by timestamp and sensor ID would require partitioning and Athena, which adds complexity. Timestream is a managed database designed for this use case, offering built-in query and retention features.

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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