This chapter provides a comprehensive overview of utility databases, elucidating their theoretical foundations, practical applications, and significance in data mining and analysis. We begin with a formal definition of utility databases, detailing their structure and the identification of transactions using set theory. Practical considerations for storing and managing utility databases on computing devices are discussed, including formatting rules and transaction storage. Additionally, we explore methods for generating synthetic utility databases, which are crucial for testing and benchmarking algorithms in data mining. Techniques for converting structured dataframes into utility databases are also covered, expanding the scope of data analysis. Furthermore, we examine how to derive and interpret statistical details of utility databases to enhance understanding of their properties and optimize their use. By integrating theoretical insights with practical skills, this chapter provides users with the procedures to effectively manage, analyze, and leverage utility databases in diverse real-world applications, laying the groundwork for advanced data analysis.