Data preprocessing is the process of preparing raw data for analysis by cleaning, organizing, transforming, and structuring it into a format suitable for data mining, machine learning, and big data analytics. Large datasets collected from multiple sources often contain missing values, duplicate records, inconsistent formats, outliers, and noisy data that can reduce the accuracy of analytical models. Preprocessing improves data quality, enhances computational efficiency, and increases the reliability of predictive models. In the context of Data Science and Big Data, preprocessing is considered one of the most critical stages because the quality of the output depends largely on the quality of the input data. The first major step is data collection and integration. Data is gathered from different sources such as databases, sensors, web applications, social media, IoT devices, and cloud storage. Since these sources may use different formats and structures, data integration combines them into a unified dataset while resolving inconsistencies, duplicate entries, and schema conflicts. This step ensures that all relevant information is available for further analysis. The second step is data cleaning. Raw data often contains missing values, incorrect records, duplicate entries, inconsistent data types, and invalid values. Missing values may be handled by deleting incomplete records, replacing them with mean, median, mode, or using predictive algorithms for estimation.