On-Premise Hadoop Just Got Easier With These 8 Hadoop-Optimized Systems

Mike Gualtieri
Vice President, Principal Analyst
August 26, 2016

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. 

Although this is a relatively new market, it promises to gain significant traction in the coming years as enterprises look to accelerate and scale big data deployments. Enterprises have some great options too choose from today from these eight vendor vendors:

  • Cisco Systems
  • Cray
  • Dell
  • HP Enterprise
  • IBM
  • NetApp
  • Oracle
  • Teradata

Whether you are looking to expand an existing Hadoop cluster or implement an entirely new one, Hadoop-Optimized Sytems are an attractive option especially compared to DIY on-premise and can be more cost and performance competive than cloud for many workloads.  Check it out the Wave for detailed evaluations . Add Hadoop-Optimized Systems to your mix of options when thinking about your impending deployment.

Hadoop Waves Galore for 2016!: Hadoop Distros, Hadoop Cloud, Hadoop-Optimized Systems

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