Sequential pattern mining is a powerful analytical tool used to uncover significant patterns within ordered data, enabling insights into recurring trends and behaviors. This chapter delves into the discovery of frequent sequence patterns in sequential databases, a process valuable across domains such as e-commerce, bioinformatics, and web usage analysis. We begin with foundational definitions and introduce the concept of sequence support as a measure of pattern significance. Leveraging the minimum support constraint, we discuss strategies to reduce search space and examine the well-known GSP algorithm to facilitate efficient pattern discovery. A practical implementation using the GSP algorithm offers insights into memory and runtime considerations critical for large datasets. This chapter equips readers with the algorithms needed to perform effective sequence pattern mining by combining theoretical foundations with practical applications, thereby enhancing data-driven decision-making in complex sequential datasets.