## 4 Comparative Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Function** & **Description** & **Example Applications** \\ \hline Data Identification and & Proficient at extracting data from complex & Optical Character Recognition for documents. \\ Extraction & sources: images, videos, PDFs, and audio. & Natural Language Processing for text analysis. \\ Data Augmentation & Generating artificial data that closely mimics the & Generating additional data points in \\ Data Augmentation & real datasets to improve the resilience of analytics & generating additional data points in \\  & and machine-learning models. Technically, it is & sparse datasets. \\ Enhancing Data Quality & Generating additional data points in & Data cleaning and preprocessing. \\  & sparse datasets. & Flagging outliers or unusual \\ Pattern Recognition and & Filling in missing values and correcting errors in & data patterns. \\ Anomaly Detection & the extracted data for higher reliability. & Streamlining data extraction from \\ Automating Extraction & Identifying patterns or anomalies in datasets & multiple sources. \\ Workflows & during extraction with the purpose of giving & Contextual analysis of text data for \\  & them special attention or processing. & streams tuned for specific domain. Triggering \\ Real-time Data & Automating repetitive tasks and optimizing & The system generates SQL queries (or other \\ Extraction & the extraction based on context and & database-specific code) for table creation, \\ Semantic & Streamlining data extraction from & including the necessary schema definitions. \\ Understanding & The system can automate the creation of & A schema structure is automatically \\ Custom Extraction for & well-optimized database schemas by analyzing the & designed, including \\ Specific Domains & data’s attributes, types, and relationships. & primary keys, foreign keys, \\ Programming Code & Generating programming code for table and & Automated actions. \\ Creation & schemas creation based on context and & The system generates SQL queries (or other \\  & data extracted. & database-specific code) for table creation, \\  & the system can automate the creation of & including the necessary schema definitions. \\ Schema Design & The system can automate the creation of & A schema structure is automatically \\  & well-optimized database schemas by analyzing the & designed, including \\  & data’s attributes, types, and relationships. & primary keys, foreign keys, \\  & and indexes. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: GenAI support for extract phase in ETL [13] (For collecting information for Table 2, the OpenAI GPT-4 model was used [13]. Prompts: “How can Generative Artificial Intelligence support extraction phase of ETL process?” and “Can you please suggest example applications and use cases?”. The output data were refined by the author and included in the submission). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Storage Name** & **Description** & **Type** & **Access** & **Source** \\ \hline Fusion Store & A virtual melting space where immediate & & GenAI, & \\  & data cleaning and profiling occur before & In-memory & Administators, & Proposed by study \\  & loading to staging. & & Developers & \\  & A storage area where raw data undergoes & & & \\  & transformations, such as cleaning, & & & \\ Staging Store & formatting, and enrichment, before being & & & \\  & loaded into the final data structures & & & \\  & accessible to end users. & & & \\  & The target data warehouse where both & & & \\  & business users and GenAI models interact & & & \\ Alliance Store & to perform business intelligence, data & & & \\  & visualizations, and other related & & & \\  & operations delivering data to end users. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stores in EGTL process. and analytics by automating and optimizing data cleansing and integration processes.