BETTER PERFORMANCE FOR SPARK ON HADOOP GUIDE

BETTER PERFORMANCE FOR SPARK ON HADOOP GUIDE

get roi

Stop Manually Tuning, Leverage Automation, and Get ROI

DevOps teams have a range of responsibilities, including managing and maintaining the health of the Hadoop production environment. DevOps teams also 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. Often, controlling spend proves difficult, and money is wasted.

By implementing automatic tuning for big data clusters, DevOps teams can eliminate costly manual tuning efforts and ensure cluster stability and efficiency. Automatic tuning can help you:

  • Achieve up to a 50 percent increase in throughput and run more jobs on existing infrastructure.
  • Reduce troubleshooting time in backlog queues and significantly reduce resource-consuming manual tuning.
  • Eliminate overspending on unnecessary hardware.
bigdata cluster

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 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.
continous tuning

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.
Curve Pattern

Take a Free Thirty-Day Trial to See What Big Data Success Looks Like

Pepperdata products provide complete visibility and automation for your big data environment. Get the observability, automated tuning, recommendations, and alerting you need to efficiently and autonomously optimize big data environments at scale.