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A company has a sales system that stores transactions as .

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A company has a sales system that stores transactions as .csv files in an Amazon S3 bucket. The S3 bucket is configured to use S3 Intelligent-Tiering. Most of the .csv files are between 64 KB and 100 KB in size. All rows and columns of the .csv files must be read when the data is processed. The company must keep the data for 5 years.

The company stores several million xsv files every day. The company must minimize the cost of storing and querying the xsv files.

Which solution will meet these requirements?

A.

Create an AWS Glue job to convert the .csv files into Apache Parquet format. Use Amazon S3 to invoke the AWS Glue job every time a .csv file arrives.

B.

Create an AWS Glue job to compress the .csv files. Schedule the AWS Glue job every hour to compress the files for the previous hour into one .csv file.

C.

Create an AWS Lambda function to convert the .csv files into Apache Parquet format. Use Amazon S3 to invoke the Lambda function every time a .csv file arrives.

D.

Create an AWS Lambda function to compress the .csv files. Use Amazon S3 to invoke the Lambda function every time a .csv file arrives.

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