In a busy urban hospital, a cardiologist noticed rising blood pressure and cholesterol levels in patients despite treatment, prompting them to turn to big data analytics. By analyzing thousands of electronic health records (EHRs), hidden trends emerged, helping identify high-risk patients who needed urgent intervention. This is the reality of how data-driven healthcare saves lives. In this context, Apollo Hospitals has developed an AI-powered heart risk score tailored to the Indian population, which has doubled the accuracy of predicting coronary diseases compared to previous models. Big data in healthcare encompasses vast and complex datasets generated from various sources, including EHRs, medical imaging, genomic data, wearable devices, and social media. These datasets are characterized by their volume, variety, and velocity, making traditional data management and analysis methods insufficient. Advanced analytics tools and techniques, such as machine learning and predictive modeling, come into play to effectively integrate, analyze, and derive meaningful insights from these diverse and massive datasets. Big data analytics in healthcare enables organizations to uncover hidden patterns, correlations, and insights that can drive better decision-making and improve patient outcomes. It contributes to personalized medicine, predictive analytics, population health management, clinical decision support, and operational efficiency. The applications of big data in healthcare are vast and diverse, spanning various domains and specialties, including precision oncology, chronic disease management, drug discovery and development, and mental health. As big data in healthcare continues to evolve, several exciting trends and advancements are on the horizon, such as artificial intelligence and machine learning, blockchain technology, and quantum computing.