Graph transactional databases are essential for modeling complex relationships in various real-world applications, such as social networks, bioinformatics, and cheminformatics. These databases can be categorized as either certain or uncertain, depending on whether their edge connections are deterministic or probabilistic. This chapter provides a comprehensive exploration of graph transactional databases, covering both theoretical and practical representations. It introduces formal definitions of graph structures using set theory, details various data storage formats (traditional and compressed), and explains procedures for converting between these formats. Additionally, it presents methodologies for generating synthetic graph databases and deriving statistical insights. Furthermore, the chapter discusses frequent subgraph pattern discovery, a crucial task for uncovering recurring structures within graph data. The use of the PAMI package is highlighted throughout, offering practical implementations for database creation, visualization, and analysis.