All 7 AWS Glue & ETL questions
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What is AWS Glue and what stages of the data pipeline process does it help automate? What is a Glue crawler, and how does it automatically populate the AWS Glue Data Catalog? How do Glue jobs use Apache Spark under the hood to perform large scale data transformations? How does AWS Glue support job scheduling and triggering ETL workflows automatically based on events or a defined schedule? How does the Glue Data Catalog serve as a shared metadata layer across multiple AWS analytics services simultaneously? How does AWS Glue support sensitive data handling through features like the Glue Data Quality and Sensitive Data Detection capabilities?
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What is AWS Glue Studio, and how does its visual interface simplify building ETL jobs for users less comfortable writing Spark code directly?

Intermediate

Answer

AWS Glue Studio provides a visual, drag and drop interface for designing ETL workflows, letting you connect data sources, transformations, and destinations visually, with Glue automatically generating the underlying Spark code needed to execute that workflow, making it accessible for data analysts or engineers who prefer a visual approach or are less experienced writing raw Spark code directly.
aws glue create-job --name visual-etl-job --command '{"Name":"glueetl","ScriptLocation":"s3://my-bucket/scripts/visual_job.py"}'
Real-world example A data analyst with limited coding experience uses Glue Studio's visual interface to build a job that joins two data sources and filters out invalid records, without needing to write any Spark code manually, since Glue Studio generates it automatically.

Common follow-ups: Can you edit the automatically generated Spark code produced by Glue Studio?;What transformation types are available directly within the Glue Studio visual editor?

Amazon QuickSight & Business Intelligence;Amazon Athena
All 7 AWS Glue & ETL questions