DEA-C01 Question 407
Single answerYou are working on a data pipeline in AWS Glue that processes a large dataset stored in Amazon S3. Before transforming the data, you need to understand its structure, quality, and statistical properties to plan your transformations effectively. Which AWS Glue feature should you use to achieve this?
- A
AWS Glue Data Catalog
- B
AWS Glue Data Profiling
- C
AWS Glue ETL Jobs
- D
AWS Glue Schema Registry
Show answer and explanation
Correct answer: B
Explanation
AWS Glue Data Profiling is a feature that enables you to analyze your dataset to understand its structure, quality, and statistical properties. This is crucial in data engineering workflows to identify issues such as null values, duplicates, or anomalies before performing transformations. While other AWS Glue features serve purposes like metadata management or schema validation, only Data Profiling provides the insights required for effective planning of data transformations.
- A. Incorrect.
The AWS Glue Data Catalog is used for metadata management and storing table definitions but does not provide insights into data quality or statistical properties.
- B. Correct.
AWS Glue Data Profiling is the correct choice since it provides insights into the structure, quality, and statistical properties of the data, helping you to plan transformations effectively.
- C. Incorrect.
AWS Glue ETL Jobs are designed for data transformations and processing, not for analyzing the quality or structure of the data prior to transformations.
- D. Incorrect.
AWS Glue Schema Registry is used to manage and validate schema versions for streaming data systems, but it does not provide any data profiling capabilities.