Pepperdata Announces Free Big Data Cloud Migration Cost Assessment

Pepperdata Eliminates Guesswork and Complexity Associated with Identifying Best Candidate Workloads down to Queue, Job and User Level, for Moving to AWS, Azure, Google Cloud or IBM Cloud

Pepperdata, the leader in big data Application Performance Management (APM), announced its new Big Data Cloud Migration Cost Assessment for enterprises looking to migrate their big data workloads to AWS, Azure, Google Cloud or IBM Cloud. By analyzing current workloads and service level agreements, the detailed, metrics-based Assessment enables enterprises to make informed decisions, helping minimize risk while ensuring SLAs are maintained after cloud migration.

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The Pepperdata Big Data Cloud Migration Cost Assessment provides organizations with an accurate understanding of their network, compute and storage needs to run their big data applications in the hybrid cloud. Analyzing memory, CPU and IO every five seconds for every task, Pepperdata maps the on-premises workloads to optimal static and on-demand instances on AWS, Azure, Google Cloud, and IBM Cloud. Pepperdata also identifies how many of each instance type will be needed and calculates cloud CPU and memory costs to achieve the same performance and SLAs of the existing on-prem infrastructure.

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“When enterprises consider a hybrid cloud strategy, they estimate the cost of moving entire clusters, but that’s not the best approach,” said Ash Munshi, Pepperdata CEO. “It’s far better to identify specific workloads that can be moved to take full advantage of the pricing and elasticity of the cloud. Pepperdata collects and analyzes detailed, granular resource metrics to accurately identify optimal workloads for cloud migration while maintaining SLAs.”

The Big Data Cloud Migration Cost Assessment enables enterprises to:

• Automatically analyze every workload in your cluster to accurately determine their projected cloud costs
• Get cost projections and instance recommendations for workloads, queues, jobs, and users
• Map big data workloads to various instance types including static and on-demand
• Compare AWS, Azure, Google Cloud, and IBM Cloud

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