The Amazon Redshift query editor v2 provides a native feature to schedule SQL queries directly from the AWS Management Console. This allows a user to write the REFRESH MATERIALIZED VIEW statement and configure a recurring schedule with minimal configuration. This approach is the most direct, integrated, and requires the least operational overhead compared to setting up external orchestration services or misusing other features. It is specifically designed for automating such SQL tasks, perfectly aligning with the "LEAST effort" requirement.

1. Amazon Redshift Database Developer Guide - Scheduling a query in the Amazon Redshift query editor v2: "You can schedule a SQL query to run on a recurring basis using the Amazon Redshift query editor v2. ... You can use this feature to automate your SQL statements

for example to refresh materialized views or run data definition language (DDL) statements." This directly confirms that option C is the intended and lowest-effort method.

2. Amazon Redshift Database Developer Guide - Creating a user-defined function: "A UDF is a scalar function that returns a single value for each row of input." This clarifies that the purpose of a UDF (including Lambda UDFs) is to return a value to a query

not to execute standalone DDL commands

making option B an incorrect application of the technology.

3. AWS Documentation - Using Amazon Redshift with AWS Glue: This documentation outlines the necessary setup for Glue to interact with Redshift

which includes creating connections and IAM roles

demonstrating a higher level of effort than the native scheduler.

4. Amazon Managed Workflows for Apache Airflow (MWAA) User Guide - Amazon Redshift operators: This guide shows that interacting with Redshift from Airflow is possible but requires provisioning and managing an entire Airflow environment

which is a high-effort solution for this specific task.