Which solution will meet these requirements?
Configure an AWS Glue Studio visual canvas to transform the data. Share the transformation steps with employees by using AWS Glue jobs.
Configure Amazon EMR Serverless to transform the data. Share the transformation steps with employees by using EMR Serverless jobs.
Configure AWS Glue DataBrew to transform the data. Share the transformation steps with employees by using DataBrew recipes.
Create Amazon Athena tables for the data. Write Athena SQL queries to transform the data. Share the Athena SQL queries with employees.
Explanations:
AWS Glue Studio is a visual interface for building ETL workflows, but it still requires code to define jobs. While it provides some lineage and transformation capabilities, it doesn’t fully meet the requirement of a prebuilt solution that doesn’t require code.
Amazon EMR Serverless is more suited for big data processing with frameworks like Apache Spark. It requires code (e.g., Spark jobs) for transformations, which contradicts the requirement for a no-code solution.
AWS Glue DataBrew is a fully managed, no-code data preparation tool that supports transformations like filtering, normalization, and aggregation. It also provides data lineage and profiling capabilities, and transformation steps (recipes) can be easily shared with others.
Amazon Athena is a query service for analyzing data in S3 using SQL. While it can transform data with SQL queries, it doesn’t offer the no-code, visual transformation capabilities, data lineage, or profiling required by the company. It also doesn’t natively support sharing transformation steps like DataBrew recipes.
I design that the answer is:
Configure AWS Glue DataBrew to transform the data. Share the transformation steps with employees by using DataBrew recipes.