## 4 Comparative Analysis  
The development of a sophisticated technical framework for the ETL process supported by GenAI is primarily based on a thorough data engineering strategy, aimed at effectively using and capitalizing on data from various sources, being able to support the volume and quality of processed data (Figure 2). Existing and widely used data engineering integration process methods are extract, transform, load (ETL) and extract, load, transform (ELT) (Table 1). This study proposes to use a variation of the ETL process, which is extract, generate, transform, load (EGTL) (Table 1). The \"generate\" step in EGTL implies an additional focus on data profiling, cleansing, or validation immediately after extraction and before the transformation process, preferably using GenAI practices. In EGTL, the focus of the processes is on the usage of custom GPT models to support all steps in the data integration process. This allows the project to have additional flexibility, utilizing the power of both structured and unstructured data, and at the same time, being able to aggregate data according to data requirements and big data project needs. Big data projects can potentially utilize the power of new disruptive technologies, and it is a matter of time until corresponding frameworks will be introduced and used by the industry. The identified problem in existing ETL processes, particularly in terms of data extraction, lies in handling the increasing volume, velocity, and variety of data sources. An examination of the documentation from AWS, Google Cloud, Microsoft Azure, and Oracle indicated that conventional ETL systems had difficulty quickly and effectively extracting data from a variety of heterogeneous sources, including both structured and unstructured data. The intricacy of data can cause problems with performance, scalability, and quality, which can affect how quickly and accurately decisions based on data are made. Integrating cutting-edge data extraction technologies, like data virtualization or real-time streaming to improve flexibility and adaptability in a variety of datasets, is a necessary part of modernizing ETL procedures to meet these difficulties. \begin{table}
\begin{tabular}{c c c} \hline
**Data Integration Process** & **Description** & **Source** \\ \hline \multirow{8}{*}{ETL} & The extract, transform, and load process relates to a systematic methodology for aggregating data from diverse origins into a singular, extensive repository, commonly referred to as a data warehouse. This procedure corresponds to a predefined series of business rules designed to refine and structure \\  & unprocessed data, rendering it suitable for subsequent storage, data analytics, and machine learning applications. Through the application of \\  & data analytics within this framework, it is feasible to cater to distinct \\  & business intelligence requisites, encompassing the predictive analysis of \\  & business decision outcomes, generation of reports and dashboards, \\  & while highlighting operational inefficiencies, among other objectives. \\  & ELT contains three steps: extract, load and transform data. The extract step \\  & involves extracting data from various sources. It is moved to a storage bin \\  & known as the \\ \multirow{2}{*}{ELT} & staging area. The load step involves moving data to the data storage area, \\  & such as a data lake or data warehouse. During the transformation step, \\  & data are de-duplicated, filtered, cleansed, formatted, decrypted or \\  & encrypted, deanonymized or anonymized, protected or hidden. \\ \hline \end{tabular}
\end{table} 
Table 1: Data integration process overview. Figure 2: EGTL model high-level solution architecture. EGTL model’s phases are represented by a distinct color: Extract (color: purple), Generate (color: yellow), Transform (color: blue), Load (color: green). The arrows between the phases represent the flow of data through each stage of the EGTL process, illustrating how data moves within the phase and from one phase to the next.