DEA-C01 · Question #243
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV…
The correct answer is C. Configure AWS Glue ETL to transform the incoming data to Apache Parquet format. Converting the CSV files into Apache Parquet during your Glue ETL jobs dramatically reduces both storage size (because Parquet is a compressed, columnar format) and query cost (because analytics engines only scan the columns you need). This change requires no new infrastructure…
Question
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis. The company wants to optimize costs for data storage and retrieval. Which solution will meet this requirement?
Options
- ATransition the data to Amazon S3 Glacier Flexible Retrieval.
- BTransition the data from Amazon S3 to an Amazon Aurora cluster.
- CConfigure AWS Glue ETL to transform the incoming data to Apache Parquet format.
- DConfigure AWS Glue ETL to use Amazon EMR to process incoming data in parallel.
How the community answered
(37 responses)- A14% (5)
- B5% (2)
- C76% (28)
- D5% (2)
Explanation
Converting the CSV files into Apache Parquet during your Glue ETL jobs dramatically reduces both storage size (because Parquet is a compressed, columnar format) and query cost (because analytics engines only scan the columns you need). This change requires no new infrastructure and pays off immediately for your daily analysis workloads.
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