One ways to learn about the structure of an existing database is to use the SQLAlchemy inspection module. It provides a simple interface to read database schema information via a Python API.

For the purpose of local testing, create a Docker container that runs the Chinook sample database on a PostgreSQL database server.

Create a Microsoft SQL Server on your local PC using Docker and load the AdventureWorks sample database so you can use it for experiments and other learning activities.

The minimum you need to know about using SQLAlchemy to build powerful SQL queries that you can use with the Pandas read_sql_query() function.

Use the Pandas data analysis framework to read data from an SQL database. In this post, I show you how to use two methods: you can read entire database tables and process them exclusively in Pandas, or you can use SQL queries to pre-process only selected database data to reduce the memory used by Pandas when analyzing the data.

Leverage Python and the pyodbc library to efficiently read data from an SQL database

Write Python programs that model a SQL database schema and read data from it, using the pyodbc driver

For the purpose of practicing data analytics programming, create your own personal database server on Microsoft's Azure cloud, populate it with sample data, and run the server on Azure's free service tier