Sequence databases, an extension of transactional databases, store ordered collections of transactions, making them invaluable for applications in healthcare, e-commerce, and web analytics. These databases structure transactions sequentially, often based on time or customer behavior, to reveal patterns that can drive socioeconomic development. This chapter introduces sequence databases by defining their mathematical representation through set theory, followed by an exploration of practical storage and implementation techniques. It details methods for generating synthetic sequence databases, which facilitate benchmarking and algorithm testing, and explains how to convert dataframes into sequential databases for broader analysis. Additionally, the chapter introduces statistical procedures for extracting critical insights, such as item frequencies and sequence length variations, from sequence databases. By combining theoretical foundations with practical applications, this chapter equips readers with essential tools for managing and analyzing sequential data, setting the stage for advanced data mining and analysis techniques.