SAA-C03 Question 357
Single answerA retail company collects customer purchase data from multiple sources, including on-premises databases and cloud-based systems. They need to clean, normalize, and transform this data into a consistent format before loading it into their Amazon Redshift data warehouse for analytics. Which service should they use to achieve this efficiently?
- A
AWS Glue
- B
Amazon Kinesis Data Streams
- C
AWS DataSync
- D
Amazon EMR
Show answer and explanation
Correct answer: A
Explanation
AWS Glue is the most suitable service for this scenario because it is designed to handle ETL (Extract, Transform, Load) workflows efficiently and can integrate seamlessly with Amazon Redshift. It simplifies the process of connecting to multiple data sources, cleaning and transforming the data, and loading it into a data warehouse for analytics. Other options like Amazon Kinesis Data Streams, AWS DataSync, and Amazon EMR are not specifically designed for this type of batch data transformation and loading use case.
- A. Correct.
AWS Glue is a fully managed data integration service specifically designed for cleaning, normalizing, and transforming data from multiple sources before loading it into a data warehouse like Amazon Redshift. It is the ideal choice in this scenario.
- B. Incorrect.
Amazon Kinesis Data Streams is primarily used for real-time streaming data processing, not for batch data transformation and loading into a data warehouse.
- C. Incorrect.
AWS DataSync is used for transferring data between on-premises storage and AWS storage services, but it does not provide capabilities for data cleaning or transformation.
- D. Incorrect.
Amazon EMR is a managed big data platform based on Hadoop and Spark. While it can handle data transformation, it is more complex to set up and manage compared to AWS Glue for this specific use case.