If the Industrial Internet were a human body, big data would be its spine. If we value a good, healthy posture, then we need to take care of that spine, and in terms of information, that means storing, managing, and using it effectively. It's not an easy task - the Industrial Internet demands quick, scalable, robust, compatible, immediate access to data with the ability to run complex analytics in a reliable, secure manner over vast distances across the globe.

In the past, data storage was intimately tied to physical media like hard drives, server arrays, and networks. With the cloud, and technologies like Hadoop, distributed storage has liberated a lot of the burdens of physical media. Distributed storage has its own challenges, though, like how to arrange data in the most optimal way for queries, redundancy, and onboard analytics.

As more businesses embrace the Industrial Internet and face these challenges, people are starting to think about data storage in new ways to meet these concerns. Here a few of the more interesting paradigms in next-gen data storage:

Software-Defined Storage

Software-defined storage is a large umbrella term, but it essentially emphasizes decoupling storage services from physical hardware. This is often accomplished through virtualized storage pools that abstract away hardware functions from physical storage media and apply them over a distributed infrastructure, giving end-user applications a consistent, easy-access experience to data.

Most approaches to big data for the Industrial Internet require the scalability, de-duplication, reliability, speed, and ease-of-access that software-defined storage is designed to offer.

Object Storage

Traditional storage extends concepts like file systems and hierarchies into the cloud, bringing whatever inefficiencies they carry. Not object storage. Object storage lacks a file system hierarchy. Instead, it stores data as objects with unique identifiers. Users access data objects by providing a corresponding code that matches up with the unique identifier.

It's more analogous to a coat check than a library's card catalog. The objects pools are abstracted away from physical media through virtual storage pools. Because of this, object storage has virtually unlimited scalability. The downside? Getting files into objects in the first place requires translation APIs, which slow down the process and reduce some of object storage's advantages.

Data-Defined Storage

Shahbaz Ali, president and CEO of Tarmin, has sought to coin a new term for modern storage. Called "data-defined" or "data-centric" storage, its idea is to structure an end-to-end approach to data management. In a data-centric approach, accessibility and content reign over traditional parameters like media, location, and cost. Removing silos and creating a single unified information architecture are its top goals. Unstructured data is automatically captured into virtualized storage pools, and virtual file systems are used to provide a bird's eye view of all data. Data-defined storage factors in mobility, data compliance, security, and backup.

As the Industrial Internet grows, there's a need to rethink how we approach its most essential support structure - big data. The benefits of data analytics are clear, but to make the most of those benefits, data storage needs to be modern, purposeful, and architected around the information itself rather than trying to accommodate traditional storage paradigms like media and hierarchy.

About the author

Suhas Sreedhar

Strategic Writer at GT Nexus