The Internet of Things (IoT) is generating a demand for the management of Big Data. There is a need for data from specialized devices and applications be made available to business applications for increased corporate value.  Businesses are integrating these specialized devices and applications with core business processes for analytics and advanced business processing. This data is a mix of varied data types and has now become corporate data. This complex data creates the demand for new technology.

Hadoop has arrived to answer the challenge. The global market revenue, which was estimated at $2.0 billion in 2013, is rapidly expanding and may grow up to a staggering $50.2 billion by 2020.

While Hadoop is offering answers to the need to handle Big Data, Hadoop requires skills and training. Finding those human resources can be challenging. Another issue is to be able to backup Big Data for the purpose of regulatory requirements or recovery of a catastrophic event.

A Big Data implementation using Hadoop presents a need for even more focus on the ability to recover from a catastrophic event quickly. However, the SMB is not often staffed or tooled to design and execute a backup strategy of this level of complexity.  The other consideration is that since the attractiveness of Hadoop is to use local servers, there is a further need to implement a data backup and recover strategy that can be managed remotely but not have a requirement that the live data be transferred to or running in a cloud environment.

There are Data Backup/Recovery Managed Service Providers (DB/R MSP) that provide remote management of the Backup process, along with professional Disaster Backup and Recovery consultation. Contracting an DB/R MSP with the model of remote DB/R management allows the SMB to maintain their data locally without the need to hire new staff or train existing staff in sophisticated data backup and recovery processes. Additionally, the SMB can have a comprehensive Data Backup and Recovery strategy while housing their Big Data locally.