Q41 — AWS SAA-C03 Ch.12

Question 41 of 100 | ← Chapter 12

Q841. A company hosts a data lake on Amazon S3. The data lake ingests data in Apache Parquet format from various data sources. The company uses multiple transformation steps to prepare the ingested data. The steps include filtering of anomalies, normalizing of data to standard date and time values, and generation of aggregates for analyses.The company must store the transformed data in S3 buckets that data analysts access. The company needs a prebuilt solution for data transformation that does not require code. The solution must provide data lineage and data profiling. The company needs to share the data transformation steps with employees throughout the company.Which solution will meet these requirements?

Correct Answer: C. Configure AWS Glue DataBrew to transform the data. Share the transformation steps with employees by using DataBrew recipes.

Explanation

To meet the requirements of a prebuilt solution for data transformation without requiring code, providing data lineage and data profiling, and sharing transformation steps with employees, the recommended solution is:C. Configure AWS Glue DataBrew to transform the data. Share the transformation steps with employees by using DataBrew recipes.Here's why:Option C suggests configuring AWS Glue DataBrew to transform the data. AWS Glue DataBrew is a visual data preparation tool that allows you to visually explore, clean, and transform data without writing code. It provides a user-friendly interface with prebuilt transformations and recipes that can be applied to the data.By leveraging DataBrew, you can perform transformation steps such as filtering anomalies, normalizing data, and generating aggregates for analysis. DataBrew offers a wide range of built-in transformations and functions that can be applied using a visual interface, eliminating the need for code.DataBrew also provides data lineage and data profiling capabilities. Data lineage allows you to track and understand the origin and flow of data, providing transparency and auditability. Data profiling helps you understand the characteristics and quality of the data being transformed.Furthermore, DataBrew allows you to share the transformation steps with employees throughout the company by using DataBrew recipes. Recipes capture the sequence of transformations and can be shared, reused, and applied to other datasets.Option A suggests configuring AWS Glue Studio to transform the data. While Glue Studio provides a visual canvas for building ETL (Extract, Transform, Load) workflows, it is more suitable for complex ETL scenarios and may require some level of coding. In contrast, DataBrew provides a simpler and more user-friendly interface for data preparation without code.Option B suggests using Amazon EMR Serverless for data transformation. While EMR Serverless can handle big data processing, it may introduce additional complexity compared to the requirements mentioned. EMR Serverless also requires coding and does not provide the same level of visual interface and prebuilt transformations as DataBrew.Option D suggests using Amazon Athena and writing SQL queries for data transformation. While Athena is a powerful query service for data analysis, it is not primarily designed for complex data transformations. Writing Athena SQL queries for each transformation step may be cumbersome and less user-friendly compared to the visual interface provided by DataBrew.Therefore, the recommended solution is to configure AWS Glue DataBrew for data transformation, as it offers a prebuilt solution with a visual interface, data lineage, data profiling, and the ability to share transformation steps with employees through DataBrew recipes.