SOLUTION BRIEFS
Pepperdata for Amazon EKS
While Kubernetes can reduce operating costs and make deployment more agile, it can also increase the management complexity of the dynamic and diverse combination of virtual machines, containers, and big data applications.
Read the solution brief to learn how Pepperdata reduces the cost of running applications on Amazon EKS by up to 40% and optimizes resources for both batch and microservices workloads.
Real-Time Optimization for Spark and Microservices
Apache Spark
on Amazon EKS
Pepperdata Capacity Optimizer for batch packs additional pending pods onto underutilized nodes, increasing node utilization and reducing the need for additional nodes—which translates directly to reduced costs.
Microservices Applications on Amazon EKS
Acting like a vertical pod autoscaler, Pepperdata Capacity Optimizer works with the Amazon EKS horizontal pod autoscaler to align pod resource requests with actual usage so that pods, workloads, and nodes can be scaled more efficiently, leading to lower costs.
TPC-DS Benchmarking
TPC-DS is the Decision Support framework from the Transaction Processing Performance Council. TPC-DS is an industry-standard big data analytics benchmark. Pepperdata’s work is an unofficial benchmark as defined by TPC. Using a 1 TB dataset on 30 nodes with 225 executors and 103 TPC-DS jobs, Pepperdata found that Capacity Optimizer Next Gen:
- Improved performance—decreased query duration by 30%
- Increased throughput—increased workload capacity by 42%