A company has an S3 folder that contains mixed file types (CSV, JSON, XLSX, and Apache Parquet). An ML engineer will use AWS Glue DataBrew to process the data and must save the final output back to S3 so AWS Glue can consume it later. Which approach satisfies these requirements?
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Correct answer: Separate the files into separate folders by file type, process each folder with DataBrew, and write the outputs in Apache Parquet format..
Why this is the answer
The correct approach is to separate files by type and process them individually, then output to Apache Parquet. DataBrew can only process one file type per job. If a dataset contains mixed file types, DataBrew will only read files of the first detected type, ignoring others. Therefore, separating the files by type into different S3 folders allows DataBrew to correctly ingest and process all the data. Apache Parquet is a highly optimized columnar storage format widely used in big data analytics and machine learning, offering efficient storage and retrieval, and is fully compatible with AWS Glue. "AWS Glue Parquet format" is not a distinct format; AWS Glue works with standard Apache Parquet.
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