Business intelligence and big data analytics workloads place high demands on the underlying infrastructure in terms of performance, capacity, reliability and security. In addition, the enormous growth of unstructured data and the Internet of Things (IoT) forces IT departments to embrace infrastructure solutions that are both cost efficient and simple to deploy. If not, they run the risk of costs spiraling out of control to support these workloads.
According to Enterprise Strategy Group (ESG), IT decision-makers responsible for business intelligence, analytics and big data classified the following factors as the most important when choosing technology solutions for business intelligence and data analytics workloads:
In addition to choosing the right database and analytics platforms, it is also essential to deploy an infrastructure that enables IT to achieve those goals. In the past, organizations often relied upon build-your-own (BYO) solutions for these very complex and demanding workloads. However, BYO turned out to be an extremely complicated and time-consuming challenge requiring cooperation and coordination across a wide range of disciplines, from database and analytics teams to compliance, security, compute, storage and networking.
Today, the emergence of converged solutions provides IT teams with a much less complex path to successful deployments—while also offering long-term benefits in reduced total cost of ownership (TCO). In addition, companies such as Dell EMC have developed converged solutions designed specifically to meet the needs of big data and analytics workloads, including systems that address the large-scale data storage needs of Hadoop data analytics. ESG makes a compelling case for using converged solutions for these workloads:
You can certainly build your own big data and IoT solutions from open source software and commodity hardware, assuming you have the time, talent and money. This approach may well be cheaper from a capital cost point of view, but it almost certainly will have significant hidden costs in staffing, support, and ongoing management of the environment. Big data and IoT as disciplines are too new, too complex, and too rapidly evolving to think that this will be easy either up front or over the years.