DEA-C01 Question 295
Select 3Your company receives daily sales data files in an Amazon S3 bucket. You need to automate the process of extracting, transforming, and loading (ETL) this data into an Amazon Redshift cluster for analytics. The solution must be serverless and scalable. Which combination of AWS services should you use to build this solution?
- A
Amazon S3 to store the raw data and AWS Glue to perform ETL tasks
- B
Amazon Kinesis Data Streams to stream data directly into Amazon Redshift
- C
AWS Glue Data Catalog to manage metadata and schema for the data
- D
Amazon Redshift COPY command to load data directly from Amazon S3
- E
Amazon EMR to process the data and write it to Amazon Redshift
Show answer and explanation
Correct answers: A, C, D
Explanation
To automate data processing in this scenario, you can use Amazon S3 to store raw input files, AWS Glue for serverless ETL, and AWS Glue Data Catalog to manage metadata. Once the data is transformed, the Amazon Redshift COPY command can load the processed data into the Redshift cluster efficiently. This combination ensures a scalable, cost-effective, and automated solution for daily processing.
- A. Correct.
Correct. Amazon S3 is used to store the raw data files, and AWS Glue can automate the ETL process, making it serverless and scalable.
- B. Incorrect.
Incorrect. Amazon Kinesis Data Streams is used for streaming data, not batch processing. This scenario involves batch processing of daily files.
- C. Correct.
Correct. AWS Glue Data Catalog is essential for managing metadata and schema for the data being processed, which is crucial for the ETL process.
- D. Correct.
Correct. The Amazon Redshift COPY command can efficiently load formatted data from Amazon S3 into a Redshift cluster.
- E. Incorrect.
Incorrect. While Amazon EMR can process large datasets, it is not the optimal solution for this serverless and automated ETL pipeline.