Can the Elephants Handle the NoSQL Onslaught?

TL;DR

This paper compares traditional RDBMSs with NoSQL systems across different workloads to evaluate performance, scalability, and future trends in the evolving big data landscape.

Contribution

It provides a comprehensive performance and scalability comparison between SQL Server and representative NoSQL systems for decision support and data-serving workloads.

Findings

NoSQL systems outperform RDBMSs in scalability for certain workloads.

Traditional RDBMSs still excel in complex decision support queries.

Performance varies significantly depending on workload type.

Abstract

In this new era of "big data", traditional DBMSs are under attack from two sides. At one end of the spectrum, the use of document store NoSQL systems (e.g. MongoDB) threatens to move modern Web 2.0 applications away from traditional RDBMSs. At the other end of the spectrum, big data DSS analytics that used to be the domain of parallel RDBMSs is now under attack by another class of NoSQL data analytics systems, such as Hive on Hadoop. So, are the traditional RDBMSs, aka "big elephants", doomed as they are challenged from both ends of this "big data" spectrum? In this paper, we compare one representative NoSQL system from each end of this spectrum with SQL Server, and analyze the performance and scalability aspects of each of these approaches (NoSQL vs. SQL) on two workloads (decision support analysis and interactive data-serving) that represent the two ends of the application spectrum. We present insights from this evaluation and speculate on potential trends for the future.