A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?
A company’s data processing pipeline uses AWS Glue jobs and AWS Glue Data Catalog. All AWS Glue jobs must run in a custom VPC inside a private subnet. The company uses a NAT gateway to support outbound connections.
A data engineer needs to use AWS Glue to migrate data from an on-premises PostgreSQL database to Amazon S3. There is no current network connection between AWS and the on-premises environment. However, the data engineer has updated the on-premises database to allow traffic from the custom VPC.
Which solution will meet these requirements?
A data engineer is designing a new data lake architecture for a company. The data engineer plans to use Apache Iceberg tables and AWS Glue Data Catalog to achieve fast query performance and enhanced metadata handling. The data engineer needs to query historical data for trend analysis and optimize storage costs for a large volume of event data.
Which solution will meet these requirements with the LEAST development effort?
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?