DEA-C01 Question 406
Single answerYou are a data engineer tasked with building an AWS Glue ETL pipeline to process customer data stored in Amazon S3. Before transforming and loading the data into an Amazon Redshift data warehouse, you want to identify patterns, missing values, and distribution of data to ensure data quality. Which AWS Glue feature can help you perform this task efficiently?
- A
AWS Glue DataBrew
- B
AWS Glue Data Catalog
- C
AWS Glue Studio
- D
AWS Glue Data Quality
Show answer and explanation
Correct answer: A
Explanation
Data profiling involves analyzing datasets to understand their structure, content, and quality. AWS Glue DataBrew is the best tool in the AWS Glue ecosystem for this purpose, enabling users to visually explore and clean data. The other options are related to different functionalities within AWS Glue but do not directly address the need for data profiling.
- A. Correct.
AWS Glue DataBrew is purpose-built for data profiling and preparation. It allows users to visually inspect data, identify missing values, and detect anomalies, making it the correct choice for this scenario.
- B. Incorrect.
AWS Glue Data Catalog is used to manage metadata about datasets but does not have built-in capabilities for data profiling.
- C. Incorrect.
AWS Glue Studio provides a graphical interface for designing and running ETL jobs but does not include dedicated data profiling capabilities.
- D. Incorrect.
AWS Glue Data Quality helps define and enforce rules to ensure data meets quality standards but is not specifically designed for data profiling tasks.