## 8 Discussion  
The integration of GenAI to all stages of the ETL process helps in data identification and extraction, data augmentation, enhancing data quality, pattern recognition, and anomaly detection, automating extraction workflows, real-time data extraction, semantic understanding, and custom extraction for specific domains. It is at a critical change, facing both unprecedented challenges and opportunities, because it possesses risks and benefits of GenAI, the assessment of which will be part of the next study. New roles and professions emerged with the rise of GenAI [38]. The integration of GenAI into existing data pipelines must be conducted responsibly and corresponding with ethical standards and guidelines. By following a structured software development lifecycle and applying outcome coding, the EGTL framework is positioned to improve data handling processes, providing a path for innovation in data engineering products. Based on the limitations and challenges highlighted in Table 12, the process encounters several significant issues during implementation, primarily revolving around the use of GenAI. One major limitation is the constraint on prompt size, where GenAI models are either programmatically or cost-limited, leading to potential hallucination effects when processing large datasets. This issue is compounded by GenAI's susceptibility to hallucination, especially when handling large volumes of data or vague prompts, which can result in the generation of incorrect or nonsensical outputs. Additionally, the variability in dataset column sizes and incorrect dataset formats further complicate the process, requiring refined prompt engineering and additional functions to manage these inconsistencies. Costs associated with frequent API calls for GenAI processing, security issues related to blocked API responses due to malicious content, and limitations on the number of requests that can be handled by the GenAI are also noted as challenges. These factors highlight the need for careful model selection, prompt segmentation, and targeted optimizations to mitigate the risks and ensure the reliability of the EGTL process. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Task** & **Total** & **Succeeded** & **Avg. Model** & **Avg. Output** & **Avg. Prompt** & **Avg. Model** \\  & **Difficulty** & **Level** & **Experiments** & **(\%)** & **(s)** & **(\%)** & **(\%)** & **Size** \\ \hline Extract & 1–3 & 120 & 87.92 & 5.41 & 100.00 & 534.0 & 942.2 \\ Generate & 1–3 & 240 & 92.98 & 4.39 & 100.00 & 399.0 & 186.0 \\ Transform & 1–3 & 120 & 88.56 & 3.16 & 98.67 & 699.0 & 178.4 \\ Load & 5 & 160 & 100.00 & 5.20 & 100.00 & 1446.0 & 34.7 \\ \hline Summary for all models & - & 640 & 92.36 & 4.54 & 99.67 & 769.3 & 335.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of all EGTL experiments. Only the minimum necessary amount of user data for system operations must be processed. The goal of the EGTL model is not to dramatically increase the data involved in the data product. Instead, the goal is to optimize and raise efficiency by providing the maximum number of meaningful insights that result in knowledge at the same time using less resources and processing less data. Missing and incorrect data can be replaced by already available data in GenAI models. As IT project personnel need adaptation and training to work effectively with new big data tools, the traditional ETL process must also evolve. One key objective of this improvement is to better navigate the complexities of modern data handling and extraction processes. The foundation, basis, and backbone of data engineering in ETL methods increasingly fall short in addressing the multifaceted demands of today's data-intensive environments. This study has introduced a novel modification to the conventional ETL process, termed EGTL--extract, generate, transfer, load.