1. 1. Data Manipulation Language (DML): The main difference between MySQL and SQLAlchemy is how they handle data manipulation language (DML). In MySQL, DML queries are typically written in SQL language, using commands such as INSERT, UPDATE, and DELETE to modify data in tables. On the other hand, SQLAlchemy provides an Object-Relational Mapping (ORM) framework, which allows developers to use Python objects and methods to manipulate data in the database. This means that in SQLAlchemy, you can perform DML operations by interacting with Python objects, without writing raw SQL queries.

2. 2. Database Agnosticism: Another key difference is the level of database agnosticism provided by MySQL and SQLAlchemy. MySQL is a specific database management system (DBMS) that operates with its own syntax and features. On the other hand, SQLAlchemy provides a higher level of abstraction, allowing developers to write code that is not specific to any particular DBMS. This means that you can write SQLAlchemy code that can be easily adapted to work with different databases, such as PostgreSQL or SQLite, without having to rewrite the entire codebase.

3. 3. Query Constructs and Expressions: MySQL and SQLAlchemy also differ in how they handle query constructs and expressions. In MySQL, queries are written as raw SQL statements, which can become complex and difficult to read, especially when dealing with advanced queries involving multiple tables. SQLAlchemy, on the other hand, provides a more intuitive and Pythonic way of constructing queries by using classes and methods for table joins, filters, and other operations. This makes the code more readable and maintainable, especially for complex queries.

4. 4. Connection Pooling and Connection Handling: MySQL and SQLAlchemy differ in how they handle database connections. In MySQL, developers have to manually manage database connections, including opening and closing connections, as well as connection pooling for optimizing performance. SQLAlchemy, on the other hand, provides built-in connection pooling and automatic connection handling, allowing developers to focus on application logic rather than managing low-level database connections. This makes it easier to handle multiple concurrent database operations in a more efficient manner.

5. 5. Migrations and Database Schema Management: When it comes to managing database schema and handling migrations, MySQL and SQLAlchemy also have different approaches. In MySQL, developers usually rely on SQL scripts or third-party tools to handle schema changes and migrations, which can be error-prone and time-consuming. SQLAlchemy, on the other hand, provides a built-in Migration API that allows developers to define and execute database schema changes using Python code. This makes it easier to version control schema changes and perform database migrations in a more structured and automated manner.

6. 6. Integration with Python Ecosystem: The last key difference between MySQL and SQLAlchemy is their level of integration with the wider Python ecosystem. MySQL is typically used as a standalone DBMS and requires specific MySQL drivers to interact with Python. SQLAlchemy, on the other hand, has a strong integration with the Python ecosystem, supporting various Python database drivers and providing a seamless integration with other libraries and frameworks, such as Flask and Django. This enables developers to leverage the full power of the Python ecosystem and easily integrate SQLAlchemy with their existing Python projects.

In Summary, MySQL and SQLAlchemy differ in their approach to data manipulation language (DML), database agnosticism, query constructs and expressions, connection pooling, migrations and database schema management, and integration with the Python ecosystem.