Enterprises agree that speedy deployment of big data Hadoop platforms has been critical to their success, especially as use cases expand and proliferate. However, deploying Hadoop systems is often difficult, especially when supporting complex workloads and dealing with hundreds of terabytes or petabytes of data. Architects need a considerable amount of time and effort to install, tune, and optimize Hadoop. Hadoop-optimized systems (aka appliances) make on-premises deployments virtually instant and blazing fast to boot. Unlike generic hardware infrastructure, Hadoop-optimized systems are preconfigured and integrated hardware and software components to deliver optimal performance and support various big data workloads. They also support one or many of the major distros such as Cloudera, Hortonworks, IBM BigInsights, and MapR. As a result, organizations spend less time installing, tuning, troubleshooting, patching, upgrading, and dealing with integration- and scale-related issues.
Choose From Among 8 Hadoop-Optimized Systems Vendors
Noel Yuhanna and me published Forrester Wave: Big Data Hadoop-Optimized Systems, Q2 2016 where we evaluated 7 of the 8 options in the market. HP Enterprise's solution was not evaluated in this Wave, but Forrester also considers HPE a key player in the market for Hadoop-Optimized Systems along with the 7 vendors we did evaluate in the Wave.
Source: Forrester Big Data Blog posts