A company produces batch data from various databases and live stream data from network sensors and application APIs. The company needs to consolidate and process all incoming data, then stage it into different Amazon S3 buckets for one-time queries and later import into a BI tool for KPIs. Which combination of steps will meet these requirements with the LEAST operational overhead? (Choose two.)

The correct options provide a serverless, managed approach with minimal operational overhead. Using AWS Lake Formation blueprints and AWS Glue for data ingestion and ETL into S3 in Parquet format is efficient. Lake Formation simplifies data lake setup, and Glue is a fully managed ETL service that can crawl diverse sources and convert data to an optimized format like Parquet for cost-effective storage and faster querying. Athena is a serverless query service perfect for ad-hoc analysis directly on S3 data, and QuickSight is a fully managed BI service for creating dashboards and KPIs. Incorrect options: Kinesis Data Analytics is for real-time analytics on streaming data, not typically for one-time queries on staged S3 data. Custom Lambda functions for moving individual records to Redshift would involve significant operational overhead for development, maintenance, and scaling, and Redshift is not ideal for consolidating diverse batch and stream data for staging before BI. AWS Glue ETL to JSON and loading into OpenSearch Service clusters would be more complex and costly than S