## 9 Conclusions and Next Steps
These results validate the capability of the EGTL model to automate complex code generation processes, contributingto DataOps principles on streamlining workflows and data reliability. Furthermore, it supports decentralized data management and facilitates a self-serve infrastructure according to the data mesh. The experiments validated the feasibility of real-time data processing, especially at lower difficulty levels, where the average model work time of 1.63 s was deemed acceptable for streaming data applications. This indicates that the EGTL model can effectively support near-real-time data processing, a critical capability in modern data-driven environments. Overall, the experiment results affirm that the EGTL model, enhanced by GenAI, can improve data quality, processing efficiency and opening new possibilities for real-time data integration and analysis, positioning it as a valuable advancement in the field of data engineering. The next step is to propose the solution architecture of the Hyperloop project decision-making ecosystem that is based on a data engineering ETGL model. A case study should be performed on exact big data projects to measure the efficiency and efficacy of EGTL. A comparison of custom GPT model usage in EGTL and use cases, the performance impact, and ethical and security considerations should be analyzed in future research. A solution architecture high-level design contributed to the need for setting up data requirements for the big data project involving complex various data sources, formats, and storages supported by GenAI. Limitations. Research does not evaluate ethical or moral considerations for the use of GenAI in the ETL process or the cultural, religious, or political aspects of EGTL model integration. The study might not address industry-specific extraction challenges comprehensively, e.g., unique regional regulatory requirements, proprietary data formats, or the sensitivity level of information being processed and its security. This study recommends using the proposed EGTL model responsibly and according to the rules, laws, and professional ethics and regulations of the area where it is applied. These limitations highlight the need for continuous research and the adaptation of ETL processes to keep pace with the evolving data landscape. The EGTL model is one aspect that shows how existing ETL processes can be improved given disruptive technologies. Specific user group access for Fusion and Alliance stores must be set based on project requirements and the best security practices and are not in the scope of the research. Future research should focus on several key areas. First, scalability testing with larger datasets and real-time applications is critical to validate the model's effectiveness in handling high data loads and streaming environments. Second, case studies applying the EGTL model to web applications should demonstrate its practical utility across diverse digital ecosystems. Additionally, gathering industry feedback through surveys will be essential for informing and guiding future refinements to the EGTL model. The following supporting information can be downloaded at: [[https://github.com/pirrencode/tsi_data_load_model/tree/main/tests/unit](https://github.com/pirrencode/tsi_data_load_model/tree/main/tests/unit)]([https://github.com/pirrencode/tsi_data_load_model/tree/main/tests/unit](https://github.com/pirrencode/tsi_data_load_model/tree/main/tests/unit)) (accessed on 23 September 2024), test_data_load.py: Test Data Load; test_logic.py: Test Logic; results.md: Test Results. [[https://github.com/pirrencode/hpl_api](https://github.com/pirrencode/hpl_api)]([https://github.com/pirrencode/hpl_api](https://github.com/pirrencode/hpl_api)) (accessed on 23 September 2024), app.py: HDME Web-application and EGTL Experiment.