A company receives a daily ~2 GB .xls file in S3 with customer data. A data engineer concatenates the first-name and last-name columns and needs to count the number of distinct customers in the file. Which approach requires the least operational effort?
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Correct answer: Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers..
Why this is the answer
AWS Glue DataBrew is the correct choice for the least operational effort. It's a visual data preparation tool that allows users to clean and normalize data without writing code. Creating a recipe in DataBrew with the COUNTDISTINCT aggregate function directly addresses the requirement to count distinct customers from the .xls file, offering a fully managed, low-code solution. Using an AWS Glue notebook with Apache Spark requires writing and managing Spark code, increasing operational overhead. While Amazon Athena with a Glue Data Catalog is suitable for querying, it still involves setting up the crawler and then writing SQL, which is more involved than DataBrew's visual interface for this specific task. Amazon EMR Serverless, while serverless, still necessitates writing and deploying Spark jobs, which is more complex than DataBrew for a simple distinct count.
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