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Deciding the deployment model is critical when enterprises adopt Hadoop. Initially, the bare metal (on-premise cluster with physical servers) model was popular to avoid I/O overhead in the virtualized ...
Deciding the deployment model is critical when enterprises adopt Hadoop. Initially, the bare metal (on-premise cluster with physical servers) model was popular to avoid I/O overhead in the virtualized environments. However, these days, cloud is also a contending option with its compelling cost savings, and ease of operation. To aid in assessing the deployment options, Accenture Technology Labs developed Accenture Data Platform Benchmark suite, a total cost of ownership (TCO) model and has tuned and compared performance of bare metal Hadoop clusters and Hadoop cloud service. Interestingly enough, the study discovered that price/performance ratio is not a critical factor in making a Hadoop deployment decision. Employing empirical and systemic analyses, the study resulted in comparable price/performance ratio from both bare metal Hadoop clusters and Hadoop-as-a-service. Moreover, cheaper purchasing options (e.g., long term contracts) provides better ratio than the bare metal one in many cases. Thus, this result debunks the idea that the cloud is not suitable to Hadoop MapReduce workloads due to their heavy I/O requirements. Furthermore, the study finds that the Hadoop default configuration provides ample headroom for performance tuning, and the cloud infrastructure enables even further performance tuning opportunities.