DEA-C01 · Question #133
A company reads data from customer databases that run on Amazon RDS. The databases contain many inconsistent fields. For example, a customer record field that iPnamed place_id in one database is…
The correct answer is B. Create an AWS Glue crawler to craw the databases. Use the FindMatches transform to find. To link customer records across inconsistent Amazon RDS databases with minimal operational overhead, the company should use AWS Glue's serverless data integration capabilities.
Question
A company reads data from customer databases that run on Amazon RDS. The databases contain many inconsistent fields. For example, a customer record field that iPnamed place_id in one database is named location_id in another database. The company needs to link customer records across different databases, even when customer record fields do not match. Which solution will meet these requirements with the LEAST operational overhead?
Options
- ACreate a provisioned Amazon EMR cluster to process and analyze data in the databases.
- BCreate an AWS Glue crawler to craw the databases. Use the FindMatches transform to find
- CCreate an AWS Glue crawler to craw the databases. Use Amazon SageMaker to construct
- DCreate a provisioned Amazon EMR cluster to process and analyze data in the databases.
How the community answered
(23 responses)- A4% (1)
- B78% (18)
- C4% (1)
- D13% (3)
Why each option
To link customer records across inconsistent Amazon RDS databases with minimal operational overhead, the company should use AWS Glue's serverless data integration capabilities.
Creating and managing a provisioned Amazon EMR cluster involves significant operational overhead for cluster management, scaling, and job orchestration, which is not the least operational overhead solution for this specific task.
AWS Glue provides serverless data integration services, and its FindMatches machine learning transform is specifically designed for entity resolution, which identifies duplicate or matching records across disparate datasets even when fields do not perfectly align, minimizing operational overhead.
While Amazon SageMaker can be used for machine learning tasks, using it to construct a custom model for data matching introduces higher development and management overhead compared to using AWS Glue's pre-built FindMatches transform for entity resolution.
Creating and managing a provisioned Amazon EMR cluster involves significant operational overhead for cluster management, scaling, and job orchestration, which is not the least operational overhead solution for this specific task.
Concept tested: AWS Glue FindMatches for entity resolution
Source: https://docs.aws.amazon.com/glue/latest/dg/machine-learning-transforms.html
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