DEA-C01 Data Ingestion and Transformation Practice Question
A logistics company ingests real-time GPS location data from thousands of delivery vehicles into Amazon Kinesis Data Streams. Each vehicle sends a JSON payload every 10 seconds containing vehicle_id, latitude, longitude, timestamp, and speed. The data must be stored in Amazon S3 for historical analysis, but the company wants to first aggregate the data per vehicle per minute (average speed, min/max coordinates) to reduce storage costs. The solution must be serverless and handle potential duplicate records without double-counting. What should the engineer do?
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 Amazon Kinesis Data Analytics for Apache Flink to aggregate data in a 1-minute tumbling window with deduplication logic, then output to Kinesis Data Firehose for delivery to S3.
Amazon Kinesis Data Analytics for Apache Flink can process streaming data with tumbling windows (1-minute) to aggregate per vehicle per minute, and Flink's stateful processing allows deduplication to avoid double-counting. The output is then sent to Kinesis Data Firehose for persistent storage in Amazon S3, all serverless. Option A (EMR with Spark Streaming) is not serverless. Option C (Kinesis Data Firehose with Lambda transformation) cannot perform stateful windowed aggregation and deduplication efficiently—it processes events individually without maintaining state across records. Option D (AWS Glue streaming ETL job) is serverless but adds higher latency and complexity compared to Kinesis Data Analytics, and is not as optimized for low-latency streaming aggregations with deduplication.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon EMR with Spark Streaming to perform the aggregation and write to S3.
Why it's wrong here
EMR requires cluster management and is not serverless.
- ✓
Use Amazon Kinesis Data Analytics for Apache Flink to aggregate data in a 1-minute tumbling window with deduplication logic, then output to Kinesis Data Firehose for delivery to S3.
Why this is correct
Flink supports windowed aggregations and stateful deduplication; Firehose delivers to S3.
- ✗
Use Kinesis Data Firehose with a Lambda transformation to aggregate records in a 1-minute window.
Why it's wrong here
Lambda in Firehose processes each record individually; it cannot maintain state across records for windowed aggregation.
- ✗
Use an AWS Glue streaming ETL job with Spark Structured Streaming to aggregate and deduplicate.
Why it's wrong here
Glue streaming ETL jobs provide at-least-once delivery semantics, meaning duplicate records can be re-processed after a job failure or restart, leading to double-counting that violates the deduplication requirement. This option is tempting because Glue streaming ETL is a serverless service that natively supports windowed aggregations and would be correct for scenarios where near-real-time transformation is needed without strict exactly-once guarantees.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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