The concept of the S-Curve is widely applicable in various industries, including business and technology. Long-term success relies on adaptation and reinvention, particularly in a dynamic world where nothing lasts forever. The database ecosystem is undergoing significant changes due to megatrends such as social media, mobile devices, cloud computing, big data analytics, IoT, machine learning, and the need for constant innovation to stay ahead of the competition. The S-Curve concept explains the evolution of successful new technologies or products, where early adopters provide initial momentum, followed by a steep ascent as more people join in, and finally, the curve levels off sharply as adoption approaches saturation. Companies that successfully scale multiple S-curves are characterized by identifying substantial market changes, possessing threshold competence before scaling up, and attracting and retaining high-performance talent. The S-Curve also applies to the evolving database and data management world, where distributed systems have emerged to power a new era of business progress, providing advantages such as scalability, performance, alignment with CPU trends, economic efficiencies, and deployment flexibility. As AI continues to augment datastores, we can expect to see natural language queries, efficient data storage, pattern recognition, and more. To jump the Database S-Curve, companies must be creative in discovering new insights and talent, adopting new products and technologies, and embracing change to stay ahead of the competition and achieve long-term success.