## 3 Related Works  
Extract, transform, load (ETL) processes are key aspects in modern data management, serving as the backend for data warehousing, analytics, and decision making in complex data projects. This chapter provides an analysis of ETL, examining its organization, impact on technology projects, and integration with modern methodologies. It also includes the effect of DataOps and GenAI on ETL. The ETL process is examined both from a data engi  
Figure 1: Research methodology overview. neering perspective and its implications in the quality of big data product corresponding to data mesh architecture. The ETL process is bound to the project solution architecture. It is structured into three core stages: extraction, transformation, and loading. Every stage has its own role in the high-level structure that ensures the integrity and usability of data. The extraction stage collects data from diverse sources. The transformation stage \"cleans\" and converts data into a format suitable for analysis. The loading stage transfers the processed data into a data warehouse. Ensuring quality in ETL is one of the primary objectives of any data project, as it has a direct effect on the underlying user data. Problems with data quality in ETL might lead to an incorrect delivery of data, analysis, privacy issues, and/or security problems. The Hyperloop Decision-Making Ecosystem, where an ECTL model will be applied and approached, is a big data software application, which uses various data sources and complex data formats and is what makes monitoring the data quality aspect particularly interesting in the scope of an ECTL compared to the traditional ETL. Domain ownership, federated governance, data as products, and a self-serve data infrastructure platform to enable platform-wide thinking are the backbone of the data mesh. They offer a decentralized approach to data architecture and organizational design, emphasizing domain-oriented data ownership, self-serve data infrastructure, and product thinking in data management [4]. Under the framework of data mesh, a data product is defined as an independent entity that encompasses all necessary elements for processing and maintaining domain-specific data for analytical or intensive data applications. Data products are structured to provide accessibility to other teams through designated output interfaces. The software development quality model for ETL embraces the following aspects: accuracy, efficiency, scalability, and maintainability. Adherence to best practices in coding, modular design, and comprehensive testing ensures high-quality ETL processes that are resilient to changes in data sources and business requirements [5]. [PERSON] highlights the importance of understanding the irregular nature of data modes alongside metadata, and the complexity of its representations make it difficult to build them in a unimodal way [6]. The data pipeline structure and its components are also affected by the irregular nature of data modes, as in the case of multimodal and complex data needed for a big data product that has corresponding requirements, e.g., a Hyperloop Decision-Making Ecosystem in the realm of intelligent transportation systems (ITS). A data pipeline in ETL includes data sources, data extraction tools, transformation engines, data loading mechanisms, and a data warehouse. Each component is optimized to handle a specific solution's tasks, ensuring an efficient flow and transformation of data according to system requirements. Modern ETL processes significantly impact technology projects by enabling efficient data handling, ensuring data quality, and facilitating informed decision-making. For the ITS realm, and more specifically for the Hyperloop Decision-Making Ecosystem, the implementation of which is part of future studies, ETL processes enable the benefits of predictive analytics, and at some point, real-time data processing by transforming raw data into valuable business insights.