The extraction, transformation, and loading (ETL) process is a crucial and intricate area of study that lies deep within the broad field of data warehousing. This specific, yet crucial, aspect of data management fills the knowledge gap between unprocessed data and useful insights. Starting with basic information unique to this complex field, this study thoroughly examines the many issues that practitioners encounter. These issues include the complexities of ETL procedures, the rigorous pursuit of data quality, and the increasing amounts and variety of data sources present in the modern data environment. The study examines ETL methods, resources, and the crucial standards that guide their assessment in the midst of this investigation. These components form the foundation of data warehousing and act as a safety net to guarantee the dependability, accuracy, and usefulness of data assets. This publication takes on the function of a useful guide for academics, professionals, and students, despite the fact that it does not give empirical data. It gives students a thorough grasp of the ETL paradigm in the context of data warehousing and equips them with the necessary skills to negotiate the complex world of data management. This program equips people to lead effective data warehousing initiatives, promoting a culture of informed decision-making and data-driven excellence in a world where data-driven decision-making is becoming more and more important.
At present, water pollution has become an important factor affecting and restricting national and regional economic development. Total phosphorus is one of the main sources of water pollution and eutrophication, so the prediction of total phosphorus in water quality has good research significance. This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform. By constructing the attribute object mapping relationship, the correlation between the two indicators was analyzed and used to predict the future data. Firstly, the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning, and the correlation between them was analyzed. Secondly, the correlation coefficients of different times and frequencies were used to predict the values for the next five days, and the data trend was predicted by python visualization. Finally, the real value was compared with the predicted value data, and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.
