MLS-C01 Question 41
Single answerYou are working as a data engineer for a company that processes large volumes of semi-structured and unstructured data. You need to build a data pipeline to transform this data and store it in an Amazon Redshift data warehouse for analytics. The data arrives daily in Amazon S3 in JSON format. Which AWS Glue feature would you use to simplify the transformation and loading process while minimizing manual coding effort?
- A
AWS Glue Data Catalog
- B
AWS Glue Crawlers
- C
AWS Glue ETL Jobs
- D
AWS Glue Studio
Show answer and explanation
Correct answer: C
Explanation
AWS Glue ETL Jobs are purpose-built to process, transform, and load data into a target destination like Amazon Redshift. In this scenario, you need to transform JSON data and load it into Redshift, making Glue ETL Jobs the correct choice. While other Glue features, such as the Data Catalog or Crawlers, are important for metadata management and schema inference, they do not perform the actual transformation and loading of data.
- A. Incorrect.
AWS Glue Data Catalog is used to store metadata about datasets, such as schema and location, but it does not handle transformations or loading of data.
- B. Incorrect.
AWS Glue Crawlers automatically infer the schema and populate the Data Catalog, but they do not perform transformations or data loading.
- C. Correct.
AWS Glue ETL Jobs are designed for transforming and loading data. They are the key feature used to process data from S3, apply transformation logic, and load it into Redshift.
- D. Incorrect.
AWS Glue Studio is a visual interface for creating ETL jobs, but it is not itself the feature that performs the transformations and loading.