DEA-C01 Data Store Management Practice Question

A data engineer is designing a data lake on Amazon S3 and needs to store data in a format that supports schema evolution and efficient columnar storage. The data will be queried using Amazon Athena and Amazon Redshift Spectrum. The engineer wants to minimize storage costs and improve query performance. Which storage format should the engineer choose?

⚠ Common exam trap

The trap here is assuming that any format supporting schema evolution is sufficient, but columnar storage is critical for analytical query performance and cost reduction.

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

✓

Apache Parquet

Apache Parquet is a columnar format that offers high compression and efficient encoding, reducing storage costs and enabling fast query performance through column pruning and predicate pushdown. It supports schema evolution and is well-integrated with Athena and Redshift Spectrum, making it the best choice.

Answer analysis

Option-by-option breakdown

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

✗
Apache Avro
Why it's wrong here
Avro is a row-based format that is efficient for write-heavy workloads and supports schema evolution, but it is not columnar. When queried by Athena or Redshift Spectrum, Avro requires reading entire rows, which can increase data scanned and reduce performance for analytical queries that access only a subset of columns. It is less optimal for the stated goals.
✗
CSV
Why it's wrong here
CSV is a simple text format that is easy to generate but does not support efficient columnar storage or advanced compression. It is row-based and lacks schema evolution capabilities. Querying CSV with Athena or Redshift Spectrum results in higher data scanned and slower performance compared to columnar formats. It is not suitable for the requirements.
✓
Apache Parquet
Why this is correct
Parquet is a columnar storage format that provides efficient compression and encoding schemes, reducing storage costs and improving query performance by allowing column pruning and predicate pushdown. It supports schema evolution and is widely used with Athena and Redshift Spectrum. This makes it ideal for the described requirements.
✗
JSON
Why it's wrong here
JSON is a text-based, row-oriented format that is human-readable but not efficient for analytical queries. It lacks columnar storage and compression benefits, leading to higher storage costs and slower query performance. Athena and Redshift Spectrum can query JSON, but they require more data scanning and parsing, which is not ideal for minimizing costs and improving performance.

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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