AWS Certified Data Engineer - Associate DEA-C01Demo

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QuestionQ126

Data Ingestion and Transformation

A data engineer needs to ingest a structured data source in .csv format into an Amazon S3 data lake. The .csv files have 15 columns. Data analysts must run Amazon Athena queries against one or two columns in the dataset. The analysts seldom query the complete file.

Which solution will satisfy these requirements in the MOST cost-effective way?

  • A Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
  • B Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.
  • C Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
  • D Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.
Explanation

Apache Parquet is a compressed columnar format. It lets Athena load only the columns required by a query, reducing the data scanned and therefore query cost when analysts generally select only one or two columns. AWS Glue ETL can transform CSV data into Parquet for storage in Amazon S3.

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