Linux Is the Best OS for Big Data Apps: 10 Reasons Why

by Darryl K. Taft

The open structure of Linux allows for expanding amounts of computing power as needed.

Open-source architectures work together, allowing computing resources to be pooled to harness large data intake.

Big data spreads massive amounts of data across a cluster of hardware to take advantage of the scaling out of compute resources. Linux’s low barrier to entry allows for these clusters to be created at a fraction of the cost.

The initial presence of a strong and free operating system made it feasible to build the initial tools for virtualization, cloud and big data. Once adoption became broader, it made sense to keep running those same tools on the platform on which they were developed: Linux.

Linux still continues to power the applications that were normally set aside for Unix servers, especially when it comes to heavy workloads. The advent of solid, high-performance and lower cost x86 servers eliminated the need to go with more expensive proprietary hardware.

The cloud delivers inexpensive storage capacity and elastically scalable, pooled computing resources that can be harnessed on demand and at very low cost. This has made it viable to perform sophisticated analytics over huge volumes of data that were never even thinkable before. Linux is the dominant OS in cloud computing.

Big data systems need computing tools that are able to work together. Linux allows a variety of tools to do so harmoniously.

As a community-developed solution, Linux comes with world-class support and stability.

Linux plays well with large-vendor solutions from companies like Teradata, IBM, Hortonworks, SAP HANA and even Microsoft.

Linux was initially released in 1991. Community-based projects are tough to out-innovate. The open-source community will continue to lead the cutting edge of big data projects.