Q61 — AWS SAA-C03 Ch.11

Question 61 of 100 | ← Chapter 11

Q761. A marketing company receives a large amount of new clickstream data in Amazon S3 from a marketing campaign. The company needs to analyze the clickstream data in Amazon S3 quickly. Then the company needs to determine whether to process the data further in the data pipeline.Which solution will meet these requirements with the LEAST operational overhead?

Correct Answer: B. Configure an AWS Glue crawler to crawl the data. Configure Amazon Athena to query the data.

Explanation

To quickly analyze the clickstream data in Amazon S3 and determine whether to process the data further in the data pipeline with the least operational overhead, the most suitable solution is:B. Configure an AWS Glue crawler to crawl the data. Configure Amazon Athena to query the data.Option B provides the desired solution with the least operational overhead:Configure an AWS Glue crawler to crawl the data. Configure Amazon Athena to query the data:\1. AWS Glue Crawler: The AWS Glue crawler automatically discovers and catalogs the data in Amazon S3. By configuring a Glue crawler, you can quickly and automatically extract the schema and metadata of the clickstream data, making it available for querying.\2. Amazon Athena: Amazon Athena is a serverless query service that allows you to analyze data directly in Amazon S3 using standard SQL queries. By configuring Amazon Athena to query the clickstream data, you can quickly analyze the data without requiring the setup and management of additional infrastructure.Here's why option B is the best choice:\1. Quick data analysis: The combination of AWS Glue crawler and Amazon Athena allows for fast and efficient analysis of the clickstream data. The Glue crawler automatically extracts the schema, making it available for querying in Athena without the need for manual schema configuration or data preparation.\2. Minimal operational overhead: AWS Glue and Amazon Athena are managed services, which means less operational overhead for your company. You don't need to provision or manage infrastructure explicitly. AWS Glue takes care of cataloging the data, and Amazon Athena handles the query execution, scaling, and performance optimization.Options A, C, and D are not the most suitable choices in this scenario:Option A suggests creating external tables in a Spark catalog and configuring jobs in AWS Glue to query the data. Although AWS Glue can be used for ETL and data preparation, configuring Spark jobs and managing a Spark catalog would introduce additional operational overhead compared to the alternative solution.Option C suggests creating external tables in a Hive metastore and configuring Spark jobs in Amazon EMR to query the data. While EMR provides a scalable platform for processing big data workloads, it requires more operational management and overhead compared to the serverless approach of using AWS Glue and Amazon Athena.Option D suggests configuring an AWS Glue crawler to crawl the data and configuring Amazon Kinesis Data Analytics to use SQL to query the data. While Kinesis Data Analytics can process and analyze streaming data, it adds complexity and operational overhead compared to the simplicity of using Amazon Athena for querying static data in S3.Therefore, the most appropriate solution to quickly analyze the clickstream data in Amazon S3 and determine whether to process the data further in the data pipeline with the least operational overhead is B: Configure an AWS Glue crawler to crawl the data. Configure Amazon Athena to query the data.