The task analyzes the mapping from source to target until it reaches transformation logic that it cannot push to the source database.

The task generates and executes a Select statement based on the transformation logic for each transformation that it can push to the database. Then, the task reads the results of the SQL query and processes the remaining transformations.

The task analyzes the mapping from target to source or until it reaches transformation logic that it cannot push to the target database.

The task generates an Insert, Delete, or Update statement based on the transformation logic for each transformation that it can push to the target database. The task processes the transformation logic up to the point where it can push the transformation logic to the database. Then, the task executes the generated SQL on the target database.

The task analyzes the mapping from source to target or until it reaches transformation logic that it cannot push to the target database.

The task generates and executes SQL statements against the source or target based on the transformation logic that it can push to the database.

You can use full SQL ELT optimization when the source and target databases are in the same relational database management system.

When you run a task with large quantities of data and full SQL ELT optimization, the database server must run a long transaction. Consider the following database performance issues when you generate a long transaction:

A long transaction uses more database resources.
A long transaction locks the database for longer periods of time, which can reduce database concurrency and increase the likelihood of deadlock.
A long transaction increases the likelihood of an unexpected event.

To minimize database performance issues for long transactions, consider using source or target SQL ELT optimization.