Cloud autoscaling provides scalability for big data deployments but not without costs. These algorithms often don’t adjust as granularly as you would expect so you may experience overprovisioning. This overprovisioning leads to wasted resources and inflated costs. An application-optimized scaling solution provides a way to optimize your resource utilization and reduce costs without requiring a complicated application implementation.
The latest benchmark results using HiBench workloads with Pepperdata Capacity Optimizer on Amazon EMR show that you can dramatically lower your costs and improve CPU and memory utilization. Capacity Optimizer quickly integrates with Amazon EMR Auto Scaling deployments. The solution makes thousands of decisions per second, analyzing the resource usage of each node to optimize CPU, memory, and I/O resources. Together Capacity Optimizer and Amazon EMR provide optimized horizontal scaling and reduced waste.
Read The Benchmark Report To See How We Can Optimize Your Big Data Analytics Stack
✔ Ensure optimal performance for all of your workloads.
✔ Reduce infrastructure costs and recapture wasted capacity.
✔ Improve cluster throughput, real-time visibility, and more.
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Pepperdata products provide complete visibility and automation for your big data environment. Get the observability, automated tuning, recommendations, and alerting you need to efficiently and autonomously optimize big data environments at scale.