IT Cost Optimization with Big Data Analytics Performance Management

IT Cost Optimization with Big Data Analytics Performance Management

IT Cost Optimization and ROI for Big Data Clusters

Improving performance, guaranteeing reliability, reducing cost, and achieving ROI is a critical priority for most data-driven companies, but it’s not easy. Managing performance on distributed systems is riddled with interdependencies and complexity. IT cost optimization strategy gets confused with IT cost-cutting, but modern IT departments have moved beyond simple expense reduction to focus on optimizing the value of their big data analytics stack investment. They understand that cost optimization isn’t a one-time action. They also know that when performance or reliability issues occur on the stack, those issues cannot typically be resolved by simply cutting expenses.

IT Cost Optimization

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Get a Complete and Transparent View of Performance and Cost

Improving business efficiency by optimizing your big data analytics stack requires the metrics to understand everything happening on the cluster in real time and getting correlated visibility across big data applications and infrastructure for a complete and transparent view of performance and cost.

Unlike solutions that merely summarize static data, Pepperdata delivers complete system analytics on hundreds of real-time operational metrics continuously collected from applications as well as the infrastructure.This includes CPU, RAM, disk I/O, and network usage metrics on every job, task, user, host, workflow, and queue.

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Simplify Troubleshooting and Reduce Time to Resolution

Complete system analytics should be viewable in a comprehensive, intuitive dashboard that provides a holistic view of cluster resources, system alerts, and dynamic recommendations for more effective troubleshooting, capacity planning, and IT chargeback reporting. This allows you to:

  • Diagnose problems quicker.
  • Automatically alert on critical conditions affecting system performance.
  • Get recommendations for rightsizing containers, queues, and other resources.
  • Leverage ML-driven resource management to recapture wasted capacity.
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Rubicon Project Improves Performance and Streamlines Automated Advertising Solution

Rubicon Project knew they could better manage their clusters, but lacked the granular insight needed to make it happen. Pepperdata Platform Spotlight provided the granular visibility necessary to quickly pinpoint, troubleshoot, and resolve problems in their cluster.

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Continuously Tune Your Hadoop Platform and Run up to 50% More Jobs

Manually tuning your applications is likely reducing your ROI due to wasted hardware resources, time, and effort. Even the most experienced IT operations teams and capacity planners can’t manually tune every application and workflow in a distributed analytics platform. The scale—thousands of applications per day and a growth rate of dozens of nodes per year—is simply too large for manual efforts to keep up.

Unlike traditional manual tuning approaches that waste man hours and overspend on unnecessary hardware, Pepperdata Capacity Optimizer provides automatic, continuous tuning for your big data analytics stack and allows you to run 30-50% more jobs on your existing Hadoop or Spark clusters. Automatically tune and optimize cluster resources, recapture wasted capacity, ensure customer satisfaction, and improve ROI.

Learn More: Observability and Continuous Tuning White Paper.

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Take the Pepperdata Interactive Demo to See What Big Data Success Looks Like

Sign up to see how automatic optimization and full-stack observability can improve performance across your entire big data stack. No data or installation is required. Simply log in to start playing in the Pepperdata sandbox.