Topic 1 Question 557
A solutions architect manages an analytics application. The application stores large amounts of semistructured data in an Amazon S3 bucket. The solutions architect wants to use parallel data processing to process the data more quickly. The solutions architect also wants to use information that is stored in an Amazon Redshift database to enrich the data.
Which solution will meet these requirements?
Use Amazon Athena to process the S3 data. Use AWS Glue with the Amazon Redshift data to enrich the S3 data.
Use Amazon EMR to process the S3 data. Use Amazon EMR with the Amazon Redshift data to enrich the S3 data.
Use Amazon EMR to process the S3 data. Use Amazon Kinesis Data Streams to move the S3 data into Amazon Redshift so that the data can be enriched.
Use AWS Glue to process the S3 data. Use AWS Lake Formation with the Amazon Redshift data to enrich the S3 data.
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- 正解だと思う選択肢: B
Option B is the correct solution that meets the requirements:
Use Amazon EMR to process the semi-structured data in Amazon S3. EMR provides a managed Hadoop framework optimized for processing large datasets in S3. EMR supports parallel data processing across multiple nodes to speed up the processing. EMR can integrate directly with Amazon Redshift using the EMR-Redshift integration. This allows querying the Redshift data from EMR and joining it with the S3 data. This enables enriching the semi-structured S3 data with the information stored in Redshift
👍 7Guru4Cloud2023/08/21 By combining AWS Glue and Amazon Redshift, you can process the semistructured data in parallel using Glue ETL jobs and then store the processed and enriched data in a structured format in Amazon Redshift. This approach allows you to perform complex analytics efficiently and at scale.
👍 5zjcorpuz2023/08/04- 正解だと思う選択肢: A👍 4ukivanlamlpi2023/08/10
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