2024/05/28 by Rishi Kesav Mohan, Mohan, Rishi Kesav, Risheek Rakshit Sukumar Kanmani +5 · 1 citation
Computer Science · #Cloud Computing and Resource Management #Databases (cs.DB) #FOS: Computer and information sciences #IoT and Edge/Fog Computing
paper · pdf · doi:10.48550/arxiv.2405.17731
openalex publication_date 2024/05/28 · openalex created_date 2024/05/30 · openalex updated_date 2026/07/28
In the era of big data, conventional RDBMS models have become impractical for handling colossal workloads. Consequently, NoSQL databases have emerged as the preferred storage solutions for executing processing-intensive Online Analytical Processing (OLAP) tasks. Within the realm of NoSQL databases, various classifications exist based on their data storage mechanisms, making it challenging to select the most suitable one for a given OLAP workload. While each NoSQL database boasts distinct advantages, inherent scalability, adaptability to diverse data formats, and high data availability are universally recognized benefits crucial for managing OLAP workloads effectively. Existing research predominantly evaluates individual databases within custom data pipeline setups, lacking a standardized approach for comparative analysis across different databases to identify the optimal data pipeline for OLAP workloads. In this paper, we present our experimental insights into how various NoSQL databases handle OLAP workloads within a standardized data processing pipeline. Our experimental pipeline comprises Apache Spark for large-scale transformations, data cleansing, and schema normalization, diverse NoSQL databases as data stores, and a Business Intelligence tool for data analysis and visualization.