It’s hard to overstate the importance of big data analytics in almost every facet of human life today. According to IBM, 2.5 million terabytes of data is being produced on a daily basis. That deluge of information represents a unique business opportunity that both large enterprises and smaller companies are rushing to take advantage of.

Some are gaining critical marketing insights from customer behavior data. Security companies are analyzing the digital output of surveillance cameras in real time to detect threats as they occur. And doctors are literally saving lives by leveraging the analysis of millions of patient records in order to produce better diagnoses and treatment regimes.

According to IDC, the big data market is expected to grow to $48.6 billion by 2019. And more and more managed IT services providers (MSPs) are positioning themselves to earn a significant portion of that revenue.

What a Big Data Analytics Solution Requires

The Oxford Dictionary defines big data as “extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.” This definition says a lot about what it takes to implement big data analytics solutions.

The first practical implication of the definition is that big data requires big storage. And because many organizations never discard data once it’s collected (new uses for archived information are discovered frequently), big data storage must be able to scale in capacity quickly, non-disruptively, and almost limitlessly.

Moreover, that storage has to be fast. In order for an analysis engine to access and correlate the relevant portions of huge datasets in a timely fashion, the storage infrastructure must exhibit extremely high levels of IOPS (Input/output Operations Per Second) performance.

 

What MSPs Bring To The Big Data Analytics Table

Obviously, performing analyses on huge datasets requires more than just putting a few extra servers and hard disk drive (HDD) arrays into an organization’s data center. Many companies, large enterprises as well as small and medium-sized businesses (SMBs), simply don’t have the time, staff, budget, or, frankly, the interest to develop and support their own big data infrastructure. MSPs that can offer such clients a high level of expertise in developing and supporting an effective big data analytics solution can provide an indispensable service.

 

MSPs Need Partners

Is it realistic to expect MSPs to acquire the comprehensive skill sets necessary for successfully implementing a big data analytics solution? Probably not. The good news, however, is that they don’t have to. The key for the vast majority of MSPs will be partnering with specialists in the technologies that make up a viable big data analytics system. This is especially true of the storage component.

For example, the fact that big data storage must be both extremely big and extremely fast imposes some seemingly incompatible requirements on the storage infrastructure. Because of the sheer amount of storage needed, there’s pressure to employ the least expensive storage technology available, which at this point is low-cost commodity hard disk drives (HDDs). The problem is that in many cases HDDs are simply too slow to deliver the necessary I/O performance.

Determining optimal solutions to such issues might be a real stretch for most MSPs. But by partnering with a world-class STaaS (storage-as-a-service) provider, MSPs gain access to a level of expertise and experience that will allow them to offer their clients state-of-the-art big data storage solutions.

 

How STaaS Can Solve Big Data Storage Issues

A first class STaaS provider, such as Zadara Storage, can help their MSP partners craft viable solutions to the most challenging big data storage issues.

For example with Zadara’s VPSA Storage Array technology, MSPs can define different storage performance tiers, using costly but very fast solid state drive (SSD) arrays for the most active data, and slower but less expensive HDDs for information that is required less frequently. In this way, the memory technology mix can be exactly right-sized to fit the required performance level, reducing storage costs to a minimum.

In addition, because Zadara Storage Clouds are physically connected to the leading public cloud providers like Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), and can be installed on-premises in customer data centers as well, VPSA storage can be located in close proximity to the servers that use the data to perform analyses. This reduces to an absolute minimum the latency issues that arise when there is a long distance between the data store and the compute engine that accesses it.

Zadara customers are never required to use capital expenses (CapEx) to purchase storage hardware. Instead, they simply pay a monthly fee for just the amount of storage they actually use. This is true whether VPSA units are installed in the cloud or on-site in the customer’s data center. In either case, the storage capacity can be seamlessly scaled up or down in seconds.

 

Clients Are Looking To MSPs For Help

Most forward-looking companies are at least intrigued by the possibilities big data analytics could open up for them. But they also understand that implementing such a solution is no trivial task. They know they need help. And that represents a tremendous opportunity for MSPs that are willing to work with expert partners to provide the capabilities their clients need.

If you’d like to know more about how partnering with Zadara Storage could make big data analytics a reality for your clients, please download the ‘STaaS vs Traditional Storage’ infographic.