As companies collect more and more data to support big data analytics, they run the risk of overloading their operations. This isn’t just an issue of having the capacity to store all that data; it’s also about having the capacity to manage all that data, including using extract, transform and load processes to make it available and backup processes to provide data protection.

Big Data Is Often Isolated

It doesn’t help that one of the most common tools for analyzing big data, Hadoop, relies on an architecture that’s very distinct from the architecture common to most enterprise applications. Hadoop relies on clusters of machines with data replicated across local storage.

These clusters are commonly separated from the servers that support transactional systems in production. They’re often underutilized, reserved for analytics jobs that don’t run on a daily basis. They often use storage technologies that differ from the core storage technology used by other application, and sometimes they end up managed by the developers rather than operations.

Big Data Has Special Demands

The different technology used for big data storage is a reasonable response to the unique needs of big data. Hadoop requires data to be local and its I/O demands are hard for storage devices to handle along with other data requests. The need for high capacity to support the volume, high performance to support intensive analytics, high reliability to support ongoing use, and scalability to support ongoing growth all drive the storage decisions.

Addressing these special demands with distinct technology choices can be costly. The solid state devices often used, due to reliability and performance, can come at higher costs. The servers used for big data aren’t virtualized, reducing their utilization and making it harder to recover from failures.

Hyperconverged Infrastructure Provides a Single Solution for Data Needs

Hyperconverged infrastructure (HCI) can provide enterprises with a storage solution that meets both big data and traditional storage needs. This means big data doesn’t stand apart from the rest of the business data and allows its storage and other systems to be part of the regular data center and operations procedures.

Hyperconverged infrastructure comes with the high availability and scalability needed for big data built right into the design. HCI implementations also make it possible to present data locally, as required by Hadoop, while using data reduction technologies to minimize the impact of multiple copies of data. Integrated data protection provides reliability.

The virtualized nodes used in HCI yield better performance. The management interfaces make it simpler to manage and monitor the devices from a single location and easily scale the Hadoop cluster when needed.

All these features benefit traditional business workloads as well as those running big data analytics. As a result, selecting hyperconverged infrastructure can help businesses converge to a single data center architecture underlying all their operations, with cost and management benefits across the organization.

Consider Nutanix Hyperconverged Infrastructure

If you see the benefits of hyperconverged infrastructure for your big data and other business workloads, consider managed Nutanix services from dcVAST. Our team will help you design and implement a Nutanix solution that meets your business needs now and will scale to meet them in the future, plus provide 24×7 support to ensure smooth operations. Contact us to learn how hyperconverged infrastructure and Nutanix can help you simplify your data center while running complex big data analytics.