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Answer: Create an AWS Glue crawler that includes a classifier that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.
Option B is CORRECT because creating an AWS Glue crawler that includes a classifier to determine the schema of all ALB access logs and write the partition metadata to AWS Glue Data Catalog automates the process of partitioning the data. This will improve query performance in Athena without requiring significant manual effort.
Author: Ritesh Yadav
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Question 34/60
The company wants to use Amazon Athena to query the logs to analyze traffic patterns.
A data engineer creates an unpartitioned table in Athena. As the amount of the data gradually increases, the response time for queries also increases. The data engineer wants to improve the query performance in Athena.
Which solution will meet these requirements with the LEAST operational effort?
A
Create an AWS Glue job that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.
B
Create an AWS Glue crawler that includes a classifier that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.
C
Create an AWS Lambda function to transform all ALB access logs. Save the results to Amazon S3 in Apache Parquet format. Partition the metadata. Use Athena to query the transformed data.
D
Use Apache Hive to create bucketed tables. Use an AWS Lambda function to transform all ALB access logs.
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