DEA-C01 Question 359
Single answerA company is building a data lake on Amazon S3 and wants to query the data using Amazon Athena. However, the data is in an uncompressed JSON format, which results in high query costs and slow performance. As a data engineer, what is the best approach to optimize query costs and performance in this scenario?
- A
Convert the JSON files to Apache Parquet format using AWS Glue or Amazon EMR before querying with Athena.
- B
Increase the instance size of Amazon Athena to improve query performance.
- C
Enable S3 Transfer Acceleration to speed up data access for Athena queries.
- D
Move the JSON files to Amazon RDS for querying and performance optimization.
Show answer and explanation
Correct answer: A
Explanation
Converting JSON to a columnar format like Apache Parquet is a common optimization strategy when using Amazon Athena. Parquet stores data in a compressed, columnar format, which reduces storage size and allows Athena to read only the relevant data, significantly improving performance and reducing query costs. Other options do not address the root issue of inefficient data format for querying.
- A. Correct.
Converting JSON files to a columnar storage format like Apache Parquet is the best practice for optimizing query costs and performance in Athena. Parquet reduces storage costs and speeds up query performance by scanning only the required columns.
- B. Incorrect.
Amazon Athena does not use instances like traditional databases; performance is optimized through data format, partitioning, and compression rather than increasing instance sizes.
- C. Incorrect.
S3 Transfer Acceleration speeds up data transfers over long distances but does not directly impact Athena query performance or costs.
- D. Incorrect.
Amazon RDS is not designed for querying large-scale, unstructured data stored in S3. It is more suitable for OLTP workloads and structured relational datasets.