Pepperdata’s client company is a global leader in design and manufacturing software that constantly analyzes large volumes of big data using Apache Spark on Amazon EMR. The client wanted to use Amazon EMR autoscaling to increase its resource capabilities by 10 times, but it needed a solution that could also cut those autoscaling costs in half.
Read the case study to discover how Pepperdata Capacity Optimizer improved this client’s big data infrastructure processes and slashed their cloud costs.
The software company found that scaling Amazon EMR resources to handle workloads resulted in runaway costs. Its goal was to reduce costs by 50 percent by increasing capacity and rightsizing compute for the company’s Apache Spark on Amazon EMR applications.
The software company used Pepperdata Capacity Optimizer for autonomous and continuous cloud cost optimization in real time, and granular visibility.
With Pepperdata, the company significantly increased its capacity and utilization for Amazon EMR workloads, optimized processes for better business results, and successfully reduced Amazon EC2 costs by over 50 percent.
Turning to Pepperdata, the 3D software company found a comprehensive solution that autonomously reduced its instance hours, maximized resource utilization of its applications, and provided visibility into its
Spark applications—all continuously and in real time. The client set a goal to reduce costs on Amazon EMR by 50 percent. With Pepperdata’s cost optimization capabilities, they were well on their way to reaching that goal.
As the enterprise’s Chief Data Architect put it:
“We didn’t have an automated way to identify potential problems or make our systems more efficient. We needed observability and insights.”