A data engineer is using AWS Glue to build an extract, transform, and load (ETL)...

Amazon Web Services Data-Engineer-Associate Question Answer

A data engineer is using AWS Glue to build an extract, transform, and load (ETL) pipeline that processes streaming data from sensors. The pipeline sends the data to an Amazon S3 bucket in near real-time. The data engineer also needs to perform transformations and join the incoming data with metadata that is stored in an Amazon RDS for PostgreSQL database. The data engineer must write the results back to a second S3 bucket in Apache Parquet format.

Which solution will meet these requirements?

Use an AWS Glue streaming job and AWS Glue Studio to perform the transformations and to write the data in Parquet format.

Use AWS Glue jobs and AWS Glue Data Catalog to catalog the data from Amazon S3 and Amazon RDS. Configure the jobs to perform the transformations and joins and to write the output in Parquet format.

Use an AWS Glue interactive session to process the streaming data and to join the data with the RDS database.

Use an AWS Glue Python shell job to run a Python script that processes the data in batches. Keep track of processed files by using AWS Glue bookmarks.

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Update on: Mar 14, 2026
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An ecommerce company stores sales data in an AWS Glue table named sales_data.

The company stores a large volume of customer records in Amazon S3.

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A data engineer is using AWS Glue to build an extract, transform, and load (ETL)...

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