The contemporary landscape of data management, marked by an unprecedented scale and velocity of data, has spurred the widespread adoption of NoSQL databases, prioritizing scalability and performance over traditional relational constraints. While offering significant flexibility, this paradigm shift introduces complex cybersecurity challenges, notably query injection vulnerabilities, which are consistently ranked among the top web application security risks. Redis, a leading in-memory key-value store powering critical infrastructure globally, presents a unique security profile due to its architectural design and features like Lua scripting. Despite its prevalence, a comprehensive academic evaluation of Redis injection attack vectors remains understudied. This study addresses this gap by systematically evaluating command and Lua script injection vulnerabilities in Redis version 7.4.1 across controlled configurations: default, password-protected, and ACL-secured environments. We quantify vulnerability risk and empirically validate mitigation strategies by employing a Dockerized testing framework, Python-driven exploit simulations, and CVSS v3.1 scoring. Our findings reveal critical weaknesses in default and permissively configured environments and demonstrate that restrictive Access Control Lists (ACLs), adhering to the principle of least privilege, provide complete mitigation against the specific injection vectors evaluated in our controlled experimental setup. We propose a Redis-specific threat taxonomy and provide empirically validated recommendations for securing Redis deployments, emphasizing layered security controls and proper ACL implementation. This research contributes the first systematic evaluation of modern Redis injection vulnerabilities and highlights the critical importance of security-conscious configurations to protect vital data infrastructure.

Health risk stratification is crucial for preventive healthcare, yet existing models often rely on binary classification generalized disease prediction, neglecting personalized health indicators and graded risk levels. Many studies apply feature selection techniques like Relief and Univariate Selection without quantifying the weighted impact of features. To address these gaps, this study introduces a Big Data-driven Health Index (HI) framework using PySpark for scalable health risk stratification. The HI is computed as a weighted sum of health-related features using SHAP Analysis, XGBoost, Random Forest, and Correlation Analysis. PySpark enables efficient processing of large-scale health data, and individuals are classified into Low and High Risk. Optimal classification thresholds are determined using the Youden Index from the ROC curve to balance sensitivity and specificity. Personalized health recommendations are generated based on risk categories to guide preventive interventions. Performance evaluation reveals that Correlation Analysis achieves 100% precision and 98.90% recall, outperforming other methods. SHAP prioritizes recall but has low precision, while XGBoost and Random Forest improve precision but struggle with recall. By leveraging Big Data techniques with PySpark, this study enhances computational efficiency, scalability, and classification accuracy, addressing prior research limitations and providing a robust data-driven approach to personalized health monitoring.