## 2 Materials and Methods  
The EGTL model is aimed to process large amounts of structured and unstructured data, operate in the scope of relational and non-relational data, build simulations, and to provide analytics to the end business users. A high-level solution architecture must be designed to include all possible (known and unknown) data requirements for complex and interconnecting systems. It supports data quality of the given product, improves user experience, and helps to choose appropriate development and data engineering tools. Big data products rely heavily on the collection, processing, integration, and analysis of vast amounts of data from various sources. Orchestration of the data lifecycle by the data engineering field plays a critical role in ensuring that these data are accurate, complete, relevant, consistent, accessible, and actionable for decision-making purposes [2]. This research methodology begins with a State-of-the-Art ETL (extract, transform, load) of existing scientific publications and industry reports to assess existing practices in data engineering related to ETL. This part of the research focuses on the ETL process organization, software development quality model, data pipeline structure and components, technological impact, data lifecycle challenges, international standards, DataOps goal and manifesto, data warehousing, and GenAI in the extraction process. The research methodology overview is visually presented in Figure 1.  
The second step in this research is to design a high-level solution architecture based on the best data engineering practices. Based on the ETL research phase findings, modifications to the existing ETL process and proposed ETL variations can be applied to enhance the existing process with GenAI. A high-level solution architecture of the product is necessary to understand what kind of input/source data the developers are dealing with, and what will be the target or highlighting solution expected at the output. Establishing a solution architecture design before drafting data requirements is strategically important and practical for several reasons. This approach ensures that the data requirements are aligned with the overall system's capabilities, constraints, and objectives. It is a foundational aspect of solution architecture in complex projects. Establishing a solution architecture first delineates the system's high-level boundaries and capabilities, ensuring that subsequent data requirements are fully compatible with the system's technological framework. This initial step is key for understanding the scope of data the system can process, guiding the formulation of data requirements that are not only feasible but also optimized for the chosen architecture. It must be a strategic, compliant, user-focused, and integrated approach to system development [3]. During this step, a cross-functional analysis of the application subject area is conducted. Data-engineering techniques of data pipeline orchestration are assessed to choose the appropriate data integration method organization and composition of data processes. A data pipeline blueprint is arranged as the basis for the solution architecture design in the final stage of research. The final step of the research methodology involves conducting an experiment on a web application prototype, specifically designed to test the proposed enhancements to the ETL process using GenAI. This step is aimed to support the EGTL technical design conclusion and serve as a bridge between the theoretical design developed in this research and its potential future practical applications. It provides empirical evidence on how the ECTL (extract, generate, transform, load) model operates within a real-world environment. The experiment focuses on integrating GenAI into various stages of data processing within the Hyperloop Decision-Making Ecosystem (HDME), assessing the model's effectiveness in improving data quality and optimizing tasks such as data extraction, generation, transformation, and analysis.