Which solution will meet these requirements?
Use AWS Site-to-Site VPN to access the on-premises Hadoop Distributed File System (HDFS) data and application. Use an Amazon EMR cluster to process the data.
Use AWS DataSync to connect to the on-premises Hadoop Distributed File System (HDFS) cluster. Create an Amazon EMR cluster to process the data.
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.
Use an AWS Snowball device to migrate the data to an Amazon S3 bucket. Create an Amazon EMR cluster to process the data.
Explanations:
Using AWS Site-to-Site VPN allows for connectivity to on-premises resources but does not reduce operational complexity. It would still require management of both on-premises and AWS resources, which complicates operations.
AWS DataSync facilitates data transfer to AWS but does not keep data processing on-premises. It introduces an additional layer of complexity and does not fulfill the requirement of maintaining an on-premises solution.
Migrating the applications to Amazon EMR clusters on AWS Outposts keeps data processing on-premises while leveraging the scalability and management simplicity of Amazon EMR. This meets the requirement for a scalable and less complex solution while remaining on-premises.
Using an AWS Snowball device for migration moves data to Amazon S3, which is a cloud service. This does not align with the requirement to keep data processing on-premises and introduces a cloud dependency.
As far as I can tell, the answer is:
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.
As far as I’m aware, the answer is:
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.