DEA-C01 · Question #236
A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in…
The correct answer is A. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use. Using a single AWS Glue ETL job to both transform the data and invoke Glue Data Quality checks lets you declaratively enforce recommended rules without spinning up separate tools. You can then write the cleansed data and, if desired, the quality metrics, directly into your…
Question
A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries. Which solution will meet these requirements MOST cost-effectively?
Options
- AUse AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use
- BUse AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to
- CUse AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use
- DUse AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to
How the community answered
(21 responses)- A71% (15)
- B10% (2)
- C5% (1)
- D14% (3)
Explanation
Using a single AWS Glue ETL job to both transform the data and invoke Glue Data Quality checks lets you declaratively enforce recommended rules without spinning up separate tools. You can then write the cleansed data and, if desired, the quality metrics, directly into your Amazon RDS for MySQL instance. This serverless, end-to-end approach minimizes service sprawl and only incurs Glue and RDS costs, making it the most cost-effective with the least operational
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