Q77 — AWS SAA-C03 Ch.10

Question 77 of 100 | ← Chapter 10

Q677. A company's applications use Apache Hadoop and Apache Spark to process data on premises. The existing infrastructure is not scalable and is complex to manage. A solutions architect must design a scalable solution that reduces operational complexity. The solution must keep the data processing on premises.Which solution will meet these requirements?

Correct Answer: C. Migrate the Apache Hadoop application and the Apache Spark application to Amazon EMR clusters on AWS Outposts. Use the EMR clusters to process the data

Explanation

To design a scalable solution that reduces operational complexity while keeping the data processing on premises with Apache Hadoop and Apache Spark, the best solution would be option C: Migrate the Apache Hadoop application and the Apache Spark application to Amazon EMR clusters on AWS Outposts. Use the EMR clusters to process the data.AWS Outposts is a fully managed service that extends AWS infrastructure, services, APIs, and tools to on- premises data centers. It allows you to run AWS services locally, providing a consistent hybrid experience.Here's how the solution works:\1. Migrate the applications: The Apache Hadoop and Apache Spark applications would be migrated from the on-premises infrastructure to Amazon EMR clusters running on AWS Outposts. This migration involves moving the code, configurations, and dependencies to the EMR clusters.\2. Use EMR clusters to process data: The data processing tasks that were previously handled by the on- premises infrastructure would now be performed by the EMR clusters running on AWS Outposts. EMR provides a fully managed big data processing platform that includes Apache Hadoop, Apache Spark, and other related tools. The EMR clusters can scale horizontally to handle large-scale data processing workloads efficiently.By migrating the applications to Amazon EMR clusters on AWS Outposts, you can achieve a scalable solution that reduces operational complexity. You benefit from the managed infrastructure and services provided by AWS, while keeping the data processing on premises.The other options are not the most suitable for meeting the requirements:A. Using AWS Site-to-Site VPN to access the on-premises Hadoop Distributed File System (HDFS) data and application, and using an Amazon EMR cluster to process the data, does not address the goal of reducing operational complexity. Additionally, it still requires managing and maintaining infrastructure on premises.B. Using AWS DataSync to connect to the on-premises Hadoop Distributed File System (HDFS) cluster and creating an Amazon EMR cluster to process the data may simplify data transfer but does not address the overall goal of reducing operational complexity. It still requires managing the on-premises infrastructure and does not leverage the benefits of AWS Outposts for running the applications.D. Using an AWS Snowball device to migrate the data to an Amazon S3 bucket and creating an Amazon EMR cluster to process the data is not a scalable on-premises solution. Snowball is typically used for data migration to the cloud, and this solution would involve transferring data back and forth between on premises and the cloud, which does not align with the requirement of keeping the data processing on premises.Therefore, the correct answer is C. Migrate the Apache Hadoop application and the Apache Spark application to Amazon EMR clusters on AWS Outposts. Use the EMR clusters to process the data.