The client is a multinational retailer with both an online presence and traditional brick-and-mortar stores.

The retailer had been successfully using Pepperdata Capacity Optimizer for nearly a decade to autonomously remove cluster waste from its on-premises data workloads. Pepperdata optimization enabled the retailer to expand operations in its on-prem data center without purchasing new hardware.

When the company migrated more than 10,000 nodes from its data center to Google Cloud Platform (GCP)—specifically Google Dataproc—it sought to realize efficiencies on GCP similar to what had been achieved on premises.

After migration, the retailer deployed Pepperdata Capacity Optimizer dynamic resource optimization on Google Cloud Platform (GCP) to autonomously eliminate idle memory across their Dataproc environment. The deployment balanced cost savings goals with the need to meet SLAs on the 10,000+ nodes where the customer runs Pepperdata across its GCP and on-premises environments.

Pepperdata Capacity Optimizer immediately decreased instance hour cost by at least 26%, resulting in additional millions of dollars per year in savings. In the future, the retailer plans to continue its partnership with Pepperdata as it expands its adoption of autoscaling and other cost-reducing and efficiency-enhancing features of the Google Cloud Platform.