DEA-C01 Question 52
Select 2Your company wants to build an ETL pipeline to process and transform JSON data stored in an Amazon S3 bucket into a relational format for analytics. The transformed data should then be stored in Amazon Redshift for querying. The ETL process must be automated, scalable, and cost-effective. Which combination of AWS services would best meet this requirement?
- A
AWS Glue for the ETL process, Amazon Redshift for data storage, and Amazon S3 as the source
- B
Amazon EMR for the ETL process, Amazon DynamoDB for data storage, and Amazon S3 as the source
- C
AWS Data Pipeline for managing workflows, AWS Glue for the ETL process, and Amazon Redshift for data storage
- D
AWS Lambda for the ETL process, Amazon RDS for data storage, and Amazon S3 as the source
- E
AWS Glue for the ETL process, Amazon Athena for querying, and Amazon S3 as the source
Show answer and explanation
Correct answers: A, C
Explanation
To meet the requirements of transforming JSON data from Amazon S3 into a relational format for analytics, AWS Glue is the best service for the ETL process due to its serverless and scalable nature. Amazon Redshift is the optimal choice for storing and querying relational data at scale. AWS Data Pipeline can also be used to manage the ETL workflow along with Glue. Other options like Amazon EMR, DynamoDB, RDS, and Athena do not completely fulfill the requirements of this scenario.
- A. Correct.
Correct: AWS Glue is a serverless ETL service that can efficiently process and transform data. Amazon Redshift is a fully managed data warehouse suitable for querying relational data, and Amazon S3 serves as the scalable source storage for JSON files.
- B. Incorrect.
Incorrect: While Amazon EMR can perform ETL tasks, DynamoDB is a NoSQL database and not suitable for analytical queries in a relational format. This does not meet the business requirement.
- C. Correct.
Correct: AWS Data Pipeline can orchestrate and automate workflows, while AWS Glue handles the ETL process, and Amazon Redshift is a suitable destination for relational analytics.
- D. Incorrect.
Incorrect: AWS Lambda is not ideal for complex ETL pipelines due to its limitations in execution time and concurrency. Amazon RDS is a relational database but not optimized for large-scale analytics like Redshift.
- E. Incorrect.
Incorrect: While AWS Glue and Amazon S3 are suitable for ETL and source storage, Amazon Athena is used for ad-hoc querying directly on S3 and does not store data in a relational format like Redshift.