A Review of Data Preprocessing Techniques in Big Data Analysis

With the full arrival of the big data era, data has gradually become a core strategic asset for scientific decision-making across industries. However, raw data often suffers from issues such as missing values, noise, inconsistencies, and redundancy due to diverse sources and inconsistent formats, which directly impair the quality and credibility of data analysis. As a critical component of the big data analysis process, data preprocessing plays a vital role in enhancing data quality and standardizing data formats. The effectiveness of preprocessing directly determines the accuracy and reliability of subsequent modeling and analysis. This paper systematically reviews and summarizes the core technologies involved in data preprocessing for big data analysis. Based on an extensive literature review and inductive analysis methods, it focuses on analyzing the fundamental principles and typical processing methods of key preprocessing steps, including data cleaning, data integration, data transformation, and data reduction. By examining practical applications in industries such as financial risk control, medical diagnosis, and e-commerce, the paper explores the real-world scenarios and outcomes of these technologies. Additionally, it delves into major challenges in current data preprocessing, including the complexity of data quality assessment, computational efficiency issues in high-dimensional data processing, and the growing importance of data privacy and security protection. The study concludes that efficient and intelligent data preprocessing is a prerequisite for fully unlocking the value of big data. Future research directions will increasingly focus on developing and optimizing automated, adaptive preprocessing technologies and integrated frameworks.