REDUCING THE RUNAWAY COSTS OF A HYBRID BIG DATA ARCHITECTURE WHITE PAPER READ MORE
Stop Manually Tuning, Leverage Automation, and Get ROI
Responsible for a myriad of responsibilities, including managing and maintaining the health of the Hadoop production environment, DevOps teams must ensure that the cluster is optimally tuned, reliable, and profitable. However, as analytics platforms grow in scale and complexity, on-prem and in the cloud, managing and maintaining performance and reliability while controlling spend is a critical challenge, and money is wasted.
By implementing automatic and continuous tuning for big data clusters, these organizations can eliminate costly manual tuning efforts while ensuring cluster stability and efficiency by:
Achieving up to a 50 percent increase in throughput and run more jobs on existing infrastructure
Reducing troubleshooting time in backlog queues and significantly resource-consuming manual tuning
Eliminating overspending on unnecessary hardware
Improve Big Data Cluster Throughput up to 50%
Even the most experienced DevOps team can’t effectively manually tune every application and workflow in a modest distributed analytics platform. The scale—thousands of applications per day and a growth rate of dozens of nodes per year—makes it impossible for manual efforts to keep up. Additionally, this combination of wasted hardware resources, time, and effort that is spent manual tuning is likely lowering your big data analytics stack ROI.
Moving beyond the traditional solutions that require manual, time-consuming application-by-application tuning, Pepperdata Capacity Optimizer uses machine learning to automatically scale system resources while providing a detailed and correlated understanding of each application using hundreds of real-time infrastructure and application metrics. Automatic, continuous tuning significantly eliminates the need for manual tuning and enables organizations to:
Recapture wasted capacity.
Automatically add tasks to servers with available resources.
Run up to 50% more jobs on your existing Hadoop or Spark clusters, meet SLAs, and get more out of your big data investment.
Continuous Tuning for the Cloud
Big data cost optimization and ROI is one of the main drivers for cloud migration. Over-provisioning for workloads is currently a given, and resource costs are a secondary concern for on-premises deployments. A typical YARN deployment allows for CPU and memory resources to be reserved and not used by applications. However, in the cloud, you pay for every minute of compute and storage resources used. This over provisioning and inefficient allocation of CPU and memory wastes resources can quickly drive up cloud costs.
Pepperdata Capacity Optimizer leverages machine learning to automatically optimize the cluster in response to inefficient CPU and memory allocation, enabling organizations to:
Reduce cloud costs by optimizing where and when your workloads run.
Recapture wasted capacity so you can run more applications and get the most out of your infrastructure investment.