Optimizing Enterprise Data Systems: A Comparative Study of SQL and NoSQL Databases, Real-Time Anomaly Detection, and Secure Containerization Techniques

Abstract

This paper presents an in-depth analysis of key components essential for optimizing enterprise data systems: SQL and NoSQL databases, real-time anomaly detection, and secure containerization techniques. SQL databases, known for their structured schemas and strong consistency models, have traditionally been the backbone of enterprise data management. However, the rise of big data and the need for flexibility in handling unstructured data have driven the adoption of NoSQL databases, which offer scalability and support for diverse data models. The paper contrasts the strengths and limitations of SQL and NoSQL databases, advocating for a polyglot persistence approach that leverages the advantages of both to meet varying enterprise needs. The discussion extends to real-time anomaly detection, a critical feature in modern data systems used to identify irregular patterns that could indicate fraud, network intrusions, or system failures. The paper explores how machine learning algorithms, when integrated with robust database architectures, can enhance the accuracy and efficiency of these systems. The paper emphasizes the importance of selecting the appropriate database system—SQL for environments requiring strict consistency, and NoSQL for high-velocity data processing. Finally, the paper delves into secure containerization techniques, crucial for the deployment of applications in cloud-native environments. It highlights best practices in container security, including isolation, access control, and runtime security, all of which are essential for maintaining the integrity of containerized applications. The analysis underscores the need for a holistic approach, combining SQL and NoSQL databases, real-time anomaly detection, and secure containerization, to create resilient and scalable enterprise data systems capable of meeting the demands of today’s digital world.