An online retail company stores Application Load Balancer (ALB) access logs in an Amazon S3 bucket. The company wants to use Amazon Athena to query the logs to analyze traffic patterns. A data…

Question

Options

• ACreate an AWS Glue job that determines the schema of all ALB access logs and writes the
• BCreate an AWS Glue crawler that includes a classifier that determines the schema of all ALB
• CCreate an AWS Lambda function to transform all ALB access logs. Save the results to Amazon
• DUse Apache Hive to create bucketed tables. Use an AWS Lambda function to transform all ALB

• A
  2% (1)
• B
  80% (40)
• C
  6% (3)
• D
  12% (6)

Explanation

As data volume grows, the primary driver of Athena query slowness is scanning too much data. An AWS Glue crawler can automatically discover the schema and partition structure of ALB access logs stored in S3 and register that structure in the Glue Data Catalog. Once partitioned tables are in place, Athena queries can use partition pruning to scan only the relevant S3 prefixes (e.g., by date or account), drastically reducing data scanned and improving performance. This requires minimal manual effort. Option A requires manually writing and maintaining a Glue job. Option C requires a Lambda transformation pipeline. Option D requires manual Hive table management plus Lambda - the highest operational burden of all options.

No community discussion yet for this question.