###### Abstract  
This paper introduces the ECTL (extract, generate, transfer, load) model, a theoretical framework designed to enhance the traditional ETL processes by integrating a novel 'generate' step utilizing generative artificial intelligence (GenAI). This enhancement optimizes data extraction and processing, presenting a high-level solution architecture that includes innovative data storage concepts: the Fusion and Alliance stores. The Fusion store acts as a virtual space for immediate data cleaning and profiling post-extraction, facilitated by GenAI, while the Alliance store serves as a collaborative data warehouse for both business users and AI processes. EGTL was developed to facilitate advanced data handling and integration within digital ecosystems. This study defines the EGTL solution design, setting the groundwork for future practical implementations and exploring the integration of best practices from data engineering, including DataOps principles and data mesh architecture. This research underscores how EGTL can improve the data engineering pipeline, illustrating the interactions between its components. The EGTL model was tested in the prototype web-based Hyperloop Decision-Making Ecosystem with tasks ranging from data extraction to code generation. Experiments demonstrated an overall success rate of 93% across five difficulty levels. Additionally, the study highlights key risks associated with EGTL implementation and offers comprehensive mitigation strategies.  
generative AI; ETL; data engineering; data mesh; DataOps; hyperloop +
Footnote †: journal:  
* How to improve the ETL process?
* How does the E(G)TL model enhance data handling and extraction in multivariate systems compared to a traditional ETL processes?
* In what ways can GenAI within the E(G)TL framework improve data quality and extract new insights from data?
* How can the E(G)TL Model be tailored to support a decentralized data architecture data mesh while adhering to DataOps principles, enhancing decision-making ecosystems?  
Central to the proposed model are the Fusion and Alliance stores. The Fusion store acts as a virtual melting pot where the data undergo cleaning and profiling immediately after extraction, facilitated by GenAI technologies. Meanwhile, the Alliance store serves as a collaborative data warehouse, supporting both business users and GenAI in a symbiotic data processing environment.  
The EGTL model is envisioned as a blueprint for the reality of modern data engineering by allowing more structured, flexible, and coherent use of GenAI in data processing. It is aligned with the best practices highlighted within the growing field of DataOps, which is a set of principles rather than a specific tool or software. This study not only explores the theoretical underpinnings of the EGTL model but also glimpses into some practical applications. While the application of EGTL is designed for use in digital ecosystem, a use case is explored in the context of data optimization within the Hyperloop technology decision-making ecosystem.  
By reimagining the ETL process through the lens of GenAI, this research contributes to the evolving discourse on data engineering, offering a comprehensive high-level framework for the efficient handling and extraction of data in multivariate systems. Exploration of the EGTL model aims to guide decision-making processes and foster innovation in the integration of AI technologies within data engineering pipelines corresponding with DataOps principles and data mesh architecture.  
The EGTL model is designed with the modern IT code of ethics supporting diverse, inclusive descriptors and terminology to ensure that the model is free from any sort of stereotypes, slang, bias, or culture assumptions.