Currently, the process of using a table variable within an SQL machine step in our workflow involves a workaround using JSON, which can lead to extended user wait times due to additional processing. Additionally, Flow's handling of table iteration within SQL is notably slower compared to direct SQL operations on large datasets. Our proposed feature request aims to streamline this process and enhance performance. We kindly request the implementation of the following enhancements to Flow's SQL machine steps:

Direct Reference to Flow Tables: Enable the ability to directly reference Flow Tables within SQL machine steps. This functionality would eliminate the need for the current workaround involving JSON encoding, leading to more efficient and faster processing.

Avoid Iteration via Machine Step Configuration: Provide an option to bypass the need for manual iteration configuration within the machine step settings. This would streamline the workflow and reduce the complexity of setting up SQL operations on Flow Tables.

Output Flexibility: Maintain the capability to output values, records, or entire tables when using Flow Tables in SQL machine steps. This flexibility is essential for various use cases and ensures seamless integration with downstream steps.