DEA-C01 Question 512
Select 3Your team is building a data lake on Amazon S3 to store structured, semi-structured, and unstructured data. To optimize query performance, you plan to use Amazon Athena for analytics. Which of the following steps will help you optimize query performance and reduce query costs when using Athena?
- A
Partition your data in S3 based on frequently queried fields.
- B
Convert your data to an optimized columnar format like Parquet or ORC.
- C
Store your data in a flat CSV file format for easier querying.
- D
Compress your data using a supported compression format like Snappy.
- E
Ensure that all data is stored in a single large S3 bucket without any partitioning to simplify Athena queries.
Show answer and explanation
Correct answers: A, B, D
Explanation
To optimize query performance and reduce costs when using Amazon Athena, it's important to partition your data in S3, convert it to optimized columnar formats like Parquet or ORC, and use compression to minimize the amount of data scanned. Avoid storing all data in a single large bucket or using flat file formats like CSV, as these practices can increase query costs and degrade performance.
- A. Correct.
Partitioning data helps Athena scan only the relevant partitions during queries, reducing data scanned and improving performance.
- B. Correct.
Columnar formats like Parquet and ORC optimize queries by storing data in a structure that is more efficient for analytical workloads, reducing the amount of data scanned.
- C. Incorrect.
Flat CSV files are not optimized for performance in Athena, as they require more storage space and scanning compared to columnar formats.
- D. Correct.
Compression reduces the amount of data stored and scanned, which improves query performance and reduces costs in Athena.
- E. Incorrect.
Storing all data in a single bucket without partitioning would increase the amount of data scanned during queries, leading to higher costs and slower performance.