DA0-002 Data Concepts and Environments Practice Question
A company is designing a data lake to store raw sensor data from IoT devices. The data arrives as JSON objects with varying schemas. Which storage approach is most appropriate?
⚠ Common exam trap
Test-takers frequently confuse 'schema-on-read' with 'schema-on-write' and assume that converting to a structured format like Avro or columnar storage is always better for performance, ignoring the requirement to store raw, varying-schema data as-is.
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
✓
Store raw JSON files in a distributed file system and apply schema-on-read
A data lake is designed to store raw data in its native format, and IoT sensor data with varying schemas is best handled by storing raw JSON files in a distributed file system (e.g., HDFS or Amazon S3). This approach leverages schema-on-read, where the schema is applied at query time rather than at write time, allowing flexibility for heterogeneous JSON objects without data loss or transformation overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ingest into a relational database with a predefined schema
Why it's wrong here
Relational databases require a fixed schema, incompatible with varying JSON.
- ✗
Store each JSON object as a separate file in a compressed columnar format
Why it's wrong here
Columnar formats like Parquet require a fixed schema, which is not suitable for varying JSON schemas.
- ✗
Convert all JSON to Avro with a fixed schema before storing
Why it's wrong here
Avro with fixed schema imposes schema-on-write, reducing flexibility.
- ✓
Store raw JSON files in a distributed file system and apply schema-on-read
Why this is correct
Schema-on-read allows handling varying schemas without upfront transformation.
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
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