MLS-C01 Question 2
Single answerA data science team is building a machine learning pipeline on AWS. They need to preprocess large amounts of semi-structured data stored in Amazon S3, such as JSON and CSV files, and then transform it into a tabular format for training a machine learning model. The team wants to use a service that scales automatically, supports distributed processing, and integrates well with other AWS services. Which AWS service should the team choose for this task?
- A
AWS Glue
- B
Amazon Redshift
- C
Amazon EMR
- D
AWS Data Pipeline
Show answer and explanation
Correct answer: A
Explanation
AWS Glue is a fully managed ETL service specifically designed to preprocess and transform data, including semi-structured formats like JSON and CSV, into tabular formats. It automatically scales for distributed processing and integrates seamlessly with Amazon S3 and other AWS services, making it the best choice for this scenario.
- A. Correct.
AWS Glue is the most appropriate service for this use case as it is a fully managed ETL (Extract, Transform, Load) service that can preprocess and transform data stored in Amazon S3. It supports distributed processing, scales automatically, and integrates well with other AWS services.
- B. Incorrect.
Amazon Redshift is a data warehouse service, which is used for querying and analyzing data, not specifically for preprocessing and transforming semi-structured data into a tabular format.
- C. Incorrect.
Amazon EMR could also perform this task, but it requires more setup and management compared to AWS Glue. AWS Glue is specifically optimized for ETL tasks and integrates more seamlessly with S3.
- D. Incorrect.
AWS Data Pipeline is primarily used for orchestrating data workflows and is not optimized for distributed data processing tasks like transforming semi-structured data into a tabular format.