BLOG: THE MANY WAYS OF BIG DATA CHARGEBACK READ MORE
Visibility and Accountability
Sometimes called “responsibility accounting”, IT chargeback is the ability to track, measure, attribute, and charge infrastructure usage costs. Implementing a chargeback model enables organizations with multi-tenant Hadoop deployments to easily view and charge business units for consumption by user, job, resource pool/queue, or department.
IT chargeback enables administrators to easily see what factors are driving costs, and how to optimize and budget accordingly. The Pepperdata IT chargeback model allows organizations to:
View the costs of shared cluster resources, including memory or CPU, over any period of time.
Generate reports on total memory and CPU used during any window in time, with a granular view into consumption and costs.
Measure CPU and memory usage using CPU core-hours and memory byte-hours.
Track Usage, Costs, and Assess Trends
Pepperdata chargeback reporting provides detailed visibility into CPU and memory usage over a time interval ranging from minutes to weeks so that you can:
Quickly understand the most costly and resource-intensive usage, calculate ROI, and make strategic IT decisions.
Which objects are using more or fewer resources over time?
Which objects are starved for resources or not using their full allocation?
Leverage the Most Detailed, Accurate Chargeback Data
One of the keys to successful IT chargeback reporting is the ability to continuously capture the performance data for every workload running on the platform. Pepperdata Platform Spotlight captures time-series data across every hardware component and application layer and makes it available within our streamlined UI, direct downloads, REST API, and data science reports. This comprehensive chargeback data set allows you to:
Easily export IT chargeback reports with one click.
Integrate big data chargeback into external reports.
Understand the full view of chargeback within your big data platform infrastructure.
Make informed and timely capacity planning decisions.