## 9 Conclusions and Next Steps  
This paper proposed a modified variation of the existing ETL data integration process that is named EGTL--extract, generate, transform, load. This method relies on instant data cleaning and profiling before the transformation step in the data pipeline during the generation stage, and this stage operates with the inclusion of GenAI on data aggregation during its extraction and later profiling. EGTL focuses on the use of GenAI custom GPT models to support handling data in all stages of the data lifecycle. EGTL supports the goal of big data product that is focused on providing knowledge as an ultimate outcome of the big data lifecycle, ensuring that a system's insights for operators and business users will be produced from both structured and unstructured data sources, utilizing various formats. One of the EGTL model's key aspects is the alignment with data mesh architecture principles by design. The incorporation of data mesh to EGTL supports enhanced data governance, emphasizing domain-oriented data ownership, self-serve data infrastructure, and product thinking in data management. Together, the EGTL model with data mesh enables a project to handle data more efficiently, foster innovation, and ensure that the data are accessible and useful across different teams and applications. In the given model, the data are treated as a product, emphasizing domain-specific ownership, which improves data quality and relevance. DataOps principles prioritize agile and lean practices to improve the quality and speed of data analytics, focusing on collaboration, automation, and continuous integration/continuous delivery pipelines for data [10]. Data mesh, alternatively, introduces a decentralized socio-technical approach, organizing data as a product, with domain-oriented, self-serve data infrastructure as a platform to enhance discoverability, accessibility, and security across corporate data stores [6]. Incorporating alternative but not contradictive modern DataOps and data mesh principles contribute to the novelty and uniqueness of the EGTL model in data engineering. Fusion, Staging, and Alliance stores correspond to EGTL customization in different stages and business groups. High-level user group access is defined for each store, highlighting access for developers, administration, quality-assurance, GenAI models and business users. Big data application business logic is considered to be orchestrated in the data lake where transformation, data processing, and data manipulation alongside machine learning algorithms are conducted. The transformation stage produces simulation data, which is loaded to the data warehouse. Simulation models are processed in the data warehouse and data are visualized, where operators and business users can access decision- making insights using an interactive dashboard. The EGTL model is designed in a way that it can handle various kinds of extraction sources, including but not limited to external databases, a project's internal databases and file systems, and the data provided by a system's user uploads using UI. Based on the results from the experiments conducted within the EGTL framework, several key findings emerged that underscore the effectiveness of integrating GenAI into the data processing pipeline. The experiments demonstrated a high overall success rate across different stages, with the loading phase achieving a perfect success rate of 100%, reflecting the robustness of the EGTL model in completing complex data management tasks. Notably, even at higher difficulty levels, models such as GPT-4 and GPT-3.5-turbo exhibited strong performance, particularly in handling more intricate data transformation tasks. The conducted code generation experiments demonstrated a high level of success, with all tasks achieving a 100% success rate. The generative AI models efficiently generated programming code for schema creation, unused structure removal, and backup table generation, significantly streamlining real-time system operations.