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Software Defined Storage Growth and What it Means for Big Data VARs

September 10, 2017

Software Defined Storage Growth and What it Means for Big Data VARs

Big data growth and software defined storage growth are in lock step. The more big data VARs can sell customers on new big data initiatives, the greater the need for software defined storage. The smart big data VARs are going to take advantage of software defined storage growth by bundling virtualization and software defined storage with big data projects.

IDC predicts that mobile computing, social media, cloud computing, and big data will be the industry trends for the foreseeable future. Social media enterprises, for example, are generating massive volumes of data that are being collected for big data projects. IDC anticipates that data volumes will exceed 6 billion terabytes this year, and that IT spending on big data projects alone will grow by 30 percent. Cloud spending (including software defined data storage) will increase by 25 percent to $100 billion. As demand for more big data storage grows, the demand for software defined storage will grow as well.

What does software defined storage growth mean for big data VARs? There are several considerations that could lead to more hardware and software sales:

A change in enterprise storage strategies

Enterprise storage systems are going to have to accommodate more agile and dynamic storage options. Software defined storage provides data storage flexibility without physical restrictions. By separating storage controls from the hardware data storage becomes modular and scalable.

It also means you can deploy different types of storage. By separating storage control from the hardware, it no longer matters what storage medium is being used. Data can be stored on RAID arrays, in the cloud, or using flash. Flash has a number of advantages, especially for real-time big data analytics, because it delivers a much faster response time.

More commodity data storage 

Since performance and data management is migrating to software, the hardware used for data storage can be much less expensive. Any x86, Power, or ARM platform can be used for data storage. Interoperability is not an issue since the hardware is separate from the software controls.

Some vendors are still fighting for custom ASICs to optimize hardware, but the industry trend is clearly to commodity server hardware managed by optimized software. And the savings are being passed on to the customer.

More cloud-based data storage

With conventional data arrays you have to trade off performance for capacity. With software defined storage hardware suddenly becomes a design choice based on whether you need performance, capacity, or, as in the case of big data, both.

With big data you need to have elastic storage to accommodate growing data sets. You also need high-speed data delivery to power real-time analytics. With software defined storage you can use cloud and hybrid-cloud storage systems to get both scale and performance; the cloud architecture gives you scalability and decoupling hardware and software gives you performance.

Combining software defined storage and virtualized storage

Although the two are often confused, software defined storage is not the same as storage virtualization. Storage virtualization integrates multiple network storage devices to look like a single entity. Software defined storage separates the physical storage medium, the hardware, from the storage controls, the software for greater control and automation.

That said, software defined storage and storage virtualization do work well together. Storage virtualization is a means of abstracting data storage. For example, you can make five different 1 terabyte disks look like a single 5 terabyte storage volume. Software defined storage has a broader definition that can include storage virtualization. Storage virtualization defines the types of data pools available for big data projects; software defined storage is a means of abstracting storage capabilities rather than capacity.

Taken together, storage virtualization can be used to create larger data pools, such as those needed for big data analytics. Software defined storage defines capabilities and services, i.e. how those pools are managed, including automated storage management and flexible access. Taken together big data VARs have almost an unlimited number of options when it comes to configuring data storage.

Lower cost of ownership

Increasingly the cost of storage solutions will be measured on their total cost of ownership over time, not their immediate costs. Using a software defined storage architecture means you no longer have to install and maintain dedicated storage systems. Virtual storage can be tapped on demand from anywhere, inside or outside the enterprise, and that means a huge savings, even in light of increasing storage demands.

As you can see software defined storage growth presents a number of sales opportunities to big data VARs. They can sell and service more hardware and software and even more cloud computing while still lowering TCO for customers.

What opportunities do you see from selling software defined data storage?