## 3 Related Works
Managing the data lifecycle in ETL involves several challenges, including (1) handling large volumes of data, (2) different data formats, (3) data security, (4) compliance with regulations, and (5) complying with data quality requirements. Addressing these challenges requires robust ETL processes, effective data governance, and adherence to data management best practices. International standards ISO/IEC 25002:2024 [7], ISO/IEC 25012 [8], and GDPR impact the existing ETL processes. ISO/IEC 25002:2024 ensures quality criteria in software applications and addressing well-known human biases. ISO/IEC 25012 sets standards for the quality of data products. The General Data Protection Regulation (GDPR) [9] provides a set of requirements and regulations for the storage and management of user data. Overall,these standards define data quality requirements and ensure adherence to data privacy and security regulations, which impact the life cycle of big data products. DataOps is a relevant new field. It was introduced in a 2014 [PERSON] publication on the IBM Big Data and Analytics Hub [10]. More than just a profession, it is a set of principles that big data projects must comply with to streamline the design, implementation, and maintenance of data pipelines in different aspects. In an ETL, DataOps facilitates continuous integration and delivery, enhances collaboration between teams, and promotes agile methodologies, leading to more efficient and reliable data pipelines. Following DataOps principles is necessary to ensure the success of big data projects [11]. The storage needs for a big data project (data warehousing) is a critical component of the ETL process. This enhanced approach to data warehousing is designed to accommodate the dynamic nature of big data, ensuring that storage solutions are not only robust and efficient but also flexible enough to adapt to the evolving needs of data analytics and processing. Modern data warehousing techniques focus on scalability, performance, and integrating diverse data types, including structured and unstructured data [12]. GenAI revolutionizes the extraction stage of the ETL by enabling automated data extraction from unstructured sources, enhancing data quality, and creating synthetic data to augment sparse datasets [13]. This leads to more efficient data processing and broader analytical capabilities. ETL processes are fundamental in shaping the landscape of web-applications processing big data. They allow organizations to harmonize disparate data sources, making the data more accessible and actionable for business intelligence, analytics, and decision-making. By streamlining the flow of data from its origin to the point of use, ETL processes facilitate improved data quality, reliability, and insights, ultimately shaping how businesses leverage data for strategic advantages [1]. The ETL process will evolve due to the increasing complexity and amount of data being used. In the context of a big data project, specifications underscore the necessity for integrating cutting-edge technologies and methodologies, in the scope of this paper--DataOps and GenAI, ensure the project's success. By organizing effectively and adhering to quality standards and international regulations, the project can play a crucial role in extracting and maximizing value from vast datasets, paving the way for innovative solutions and enhanced decision-making processes. Therefore, it is proposed to develop a modified version of the ETL process as a model that can comply with modern tools and principles aiding in development of the big data project. The main limitation of ETL tools working \"outside of the box\" is that they were designed to work in \"batch\" data processing modes: collect data, download them, collect more data, download them, etc. In some situations, batch data loading works well. Cross-Platform Conversion Tools may be difficult to implement for cross-platform data sources, especially where big data collection is used.