Exploring Data Warehousing Capabilities of NoSQL Systems for Big Data Analytics

Abstract

In the current era of massive data expansion, a suitable database management system (DBMS) must be chosen to handle Big Data analytics queries effectively. In this work, a comparative evaluation is carried out between PostgreSQL, a popular RDBMS, and MongoDB, a well-known NoSQL database. By studying six large datasets, we identified the best use cases and key efficiency indicators for each of the systems. Through careful analysis of individual query execution time and system throughput, it shows the advantages that NoSQL systems offer when dealing with huge datasets. It specifically assesses the scalability, speed, and adaptability to manage real-world information movements akin to typical analytical work. The comparative analysis that follows highlights differences in data ingestion speeds, query execution speeds and system flexibility for diverse data access and analysis requirements. The findings serve as a useful compass for database selection in data warehousing and Big Data analytics projects, emphasizing the pros and cons of NoSQL systems. It presents solid results and performance indicators to empower stakeholders to make informed decisions in accordance with their specific data management needs and analytical goals. A key finding of the paper is that matching NoSQL database and query needs with organizational structure is important to maximize data analytics results across a range of organizational scenarios.