This chapter describes several techniques and considerations in biomedical data preprocessing to ensure data quality, integrity, and suitability for analysis. It discusses common challenges in biomedical datasets, including complexity, heterogeneity, and the prevalence of missing or inconsistent data. Methods for assessing and improving data quality are discussed, along with strategies for handling missing data to minimize its impact on analyses. The chapter also covers data transformation techniques such as normalization, encoding, and dimensionality reduction, as well as approaches to manage outliers effectively. Data integration methods are examined to address challenges with duplicate data and metadata management. Finally, privacy and security practices are discussed.