This thesis introduces a framework for creating and managing dynamic data processing pipelines, focusing on the generation of self-sufficient executable Python code. The framework is designed to allow for the efficient description, compatibility, and execution of data processing tasks. It addresses the need for flexible pipeline creation in modern data environments, where existing tools often fall short in adaptability. Key aspects of the framework include: Dynamic pipeline creation based on user inputs, allowing for quick adjustments to workflows as data sources evolve. Ease of creating new processing blocks, which are self-contained executable Python scripts, enabling the system to expand its capabilities over time.