Which combination of steps will meet these requirements with the LEAST operational overhead?

Explanations:

Amazon Athena allows for serverless querying of data directly from Amazon S3, making it easy to run one-time queries without the need for additional infrastructure management. Amazon QuickSight can visualize the queried data by creating dashboards for KPIs, providing a seamless way to analyze and present the data.

Amazon Kinesis Data Analytics is designed for processing streaming data in real-time, not for one-time queries. While it could be used for analyzing live stream data, it is not suitable for the requirement of running one-time queries on consolidated data, making this option less aligned with the need.

Creating custom AWS Lambda functions to move records to an Amazon Redshift cluster introduces operational overhead due to the need to manage the functions, monitor performance, and handle error scenarios. Additionally, this option does not directly address staging the data into S3 buckets as required.

While AWS Glue can transform data, loading data into multiple Amazon OpenSearch Service clusters adds complexity and overhead. The requirement focuses on consolidating data into S3 buckets for later queries, and this option does not directly facilitate that process effectively.

Using blueprints in AWS Lake Formation simplifies the data ingestion process into a data lake, and AWS Glue can crawl the data sources and load data into Amazon S3 in Apache Parquet format, which is efficient for analytics. This approach minimizes operational overhead while ensuring the data is properly staged for analysis.