DAS-C01 · Question #158
A company is reading data from various customer databases that run on Amazon RDS. The databases contain many inconsistent fields. For example, a customer record field that is place_id in one…
The correct answer is B. Create an AWS Glue crawler to crawl the databases. To link customer records across multiple Amazon RDS databases with inconsistent field names and achieve the least operational overhead, creating an AWS Glue crawler is the most suitable first step. This service automatically infers schemas and populates the AWS Glue Data…
Question
A company is reading data from various customer databases that run on Amazon RDS. The databases contain many inconsistent fields. For example, a customer record field that is place_id in one database is location_id in another database. The company wants to link customer records across different databases, even when many customer record fields do not match exactly. Which solution will meet these requirements with the LEAST operational overhead?
Options
- ACreate an Amazon EMR cluster to process and analyze data in the databases
- BCreate an AWS Glue crawler to crawl the databases
- CCreate an AWS Glue crawler to crawl the data in the databases
- DCreate an Amazon EMR cluster to process and analyze data in the databases.
How the community answered
(40 responses)- A13% (5)
- B73% (29)
- C10% (4)
- D5% (2)
Why each option
To link customer records across multiple Amazon RDS databases with inconsistent field names and achieve the least operational overhead, creating an AWS Glue crawler is the most suitable first step. This service automatically infers schemas and populates the AWS Glue Data Catalog, providing a unified metadata store essential for understanding and resolving data inconsistencies.
Creating an Amazon EMR cluster involves managing a distributed computing cluster, which has significantly higher operational overhead compared to a serverless AWS Glue crawler.
An AWS Glue crawler automatically connects to data stores, infers schemas, and populates the AWS Glue Data Catalog with metadata. This catalog helps identify and manage schema inconsistencies like different field names, laying the groundwork for subsequent data cleaning and linking with minimal operational overhead as it is a serverless component.
This option is effectively the same as B but is indicated as a wrong answer in the provided question format, likely due to a subtle distinction or ordering not apparent in the text.
Creating an Amazon EMR cluster involves managing a distributed computing cluster, which has significantly higher operational overhead compared to a serverless AWS Glue crawler.
Concept tested: Schema inference and data cataloging
Source: https://docs.aws.amazon.com/glue/latest/dg/components-overview.html#crawlers-overview
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