Predictions for 2016: Trends that Will Transform the Big Data Storage Industry

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In late 2014 I made some predictions about two trends that would shake up the storage industry over the next year and eventually phase out IT SAN and NAS storage as we know it.

SaaS Storage

More and more small- and medium-sized organizations would delegate not only software but storage to SaaS providers such as Microsoft Office 365, Salesforce and Box, and so wouldn’t be buying much traditional storage from now on. SaaS providers would continue forgoing the EMC, Netapp SAN/NAS storage model for the hyperscale, software-defined, hardware independent, shared nothing architecture model of Facebook, Amazon, and Google. The result: The traditional storage industry players such as EMC and Netapp could wave goodbye to about $20 billion.

Performance and Scalable storage

Larger enterprises and other data intensive organizations were reaching a fork in the storage road leading them to run their own software-defined disk storage for scale and silicon in the form of flash arrays, hybrid storage and converged storage for performance hungry, latency sensitive applications such as streaming video or financial modeling.

The result: the traditional storage players would see their market slashed by startups in these two emerging categories.

One year later these trends are taking shape, as the revenues and profitability of legacy enterprise storage giants EMC, Netapp and IBM continue taking big revenue hits. In the second quarter of 2015 EMC’s revenues declined 4 percent compared to last year, Netapp’s 19.6 percent and IBM’s a whopping 28.9 percent, according to IDC. This year two other emerging trends promise to accelerate the process:

Deep Learning

Big Data Analytics have been around for a few years, digging through huge volumes of information from enterprise databases, files, and emails as well as social media and consumer purchases to recognize small and big hidden trends to exploit for competitive advantage, predictive maintenance, and other purposes. These and other strategies save organizations millions, enhance revenues and improve services and the general quality of life.

Deep Learning takes off from Big Data, incorporating the artificial intelligence and neural network movements of yore to yield systems that can harness information, multilayered algorithms, and software to actually mimic human learning. These systems can teach themselves to, for example, understand spoken commands, sort through photos, recognize objects and faces, discover potential new drugs or a carry out a host of other breakthrough functions on their own, automatically. Today you can even find robots that can walk and learn any number of new things by example–after example after example, after example.

The Emerging Internet of Things

(IOT) promises to transform the Web from a medium of human and information interaction to interaction among appliances, machines, components, systems and humans–with humongous volumes of information produced daily. Just a single airline flight can produce a terabyte or more of IoT information from scores of airplane component sensors. When you imagine one hundred thousand flying airplanes producing a daily total of 100 petabytes of data daily, the volume of information to be mined for trends, maintenance issues and other revelations large and small is almost unimaginable.

Deep learning, Big Data and IoT will save lives by preventing airplane and automobile crashes, diagnosing and treating patients, discovering powerful new drugs and on and on.

In fact in more and more industries the value of information and software has begun outgrowing the value of the traditional products manufacturers and retailers have built and sold for years. Many new types of businesses have and will be created just to harness this information, while sharing information across industries and industry players in manufacturing, distribution and other sectors will lead to powerful new innovations.

In such a data/information hungry environment, in which every aspect of corporations’ and our lives is data driven and all data past, present, and future is a potential gold mine of business and other insight, traditional limited scale storage models are doomed in favor of hyperscale and performance. Hyperscale is necessary to store and harness years and endless quantities of Big Data-, IoT- and Deep-Learning-related information nearby continuously for quick analysis. Performance is the goal when real time and low latency are required.

This level of scalability can only come from software defined storage, just as compute and networks have moved to an intelligent, scale out software defined architecture.

In the traditional IT storage environment, scale leads inevitably to complexity, instability and performance issues. However, the hyperscale, storage defined, shared nothing model is well on the way to achieving:

Technical Scalability, in which systems become more stable and reliable as they grow.

Operational Scalability where systems become simpler, not more difficult, to manage.

Performance Scalability where system performance scales as the system scales

Cost Scalability System cost falls as the systems scale

Time Scalability Today, a lifetime or more is the time frame for storing and accessing information, rather than a few weeks, months, or years. I’ve met with people who want their Facebook posts to be conserved after they’ve passed.

This is what Google Amazon and Facebook have already achieved and the architecture that Scality RING is providing to a host of organizations looking for a hyperscale storage solution. I can only remember one time I was unable to load my Facebook page. Recently Facebook surpassed 1 billion users daily. While we at Scality provide the software to store the data at a massive scale, others will continue finding new, breakthrough ways harness it and present it to you.

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