A company needs a no-code way to transform a large Parquet dataset in Amazon S3 by applying one-hot encoding to some columns and write the transformed data back to the same S3 bucket. Which solution meets these constraints?
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Correct answer: Create an AWS Glue DataBrew project that connects to the S3 data, author a recipe to one-hot encode the columns, and run a job to write the output back to S3..
Why this is the answer
AWS Glue DataBrew is the correct solution because it is a visual data preparation tool that allows users to clean and normalize data without writing any code. It directly supports one-hot encoding as a pre-built transformation within its recipe builder and can read from and write back to Amazon S3. Amazon Athena SQL and Amazon Redshift Spectrum are SQL-based query services. While they can read Parquet from S3, performing one-hot encoding directly in SQL is complex, often requiring multiple joins or case statements, and is not a "no-code" solution. AWS Glue ETL interactive notebooks require writing PySpark or Scala code, which violates the "no-code" constraint.
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