Understanding SAP Data Architecture from a Python Perspective

Embarking on data analysis or machine learning projects within an SAP environment using Python requires a foundational understanding of how SAP systems structure and manage data. Unlike simpler databases, SAP landscapes are complex ecosystems, built over decades with various modules and technologies. Before we write a single line of connection code, grasping this architecture from a data consumer's perspective is paramount. It dictates how efficiently and effectively you can access the insights locked within.

At its core, SAP data resides in databases, but the path to reaching that data is often layered. Traditional SAP systems (like SAP ECC) relied on various database platforms, while modern SAP S/4HANA is built exclusively on the SAP HANA in-memory database. This evolution significantly impacts data access performance and the types of data structures available for direct querying.