Big data in the cloud has a lot of moving parts, overlap, and sprawling interdependencies that make understanding cloud resource usage a challenge. Pepperdata helps you leverage cloud visibility deployments, accelerate your cloud adoption, streamline IT operations, and deliver great customer experiences.
Pepperdata for Amazon EMR provide full-stack observability, automated tuning, and real-time insights across all of your EMR instances—all in one place. Automatically optimize your big data and improve cloud price/performance by up to 3X.
Magnite knew they could better manage their clusters, but lacked the granular insight needed to make it happen. Pepperdata Platform Spotlight gave them the granular visibility necessary to quickly pinpoint, troubleshoot, and resolve problems in their cluster.
Cloud providers provision infrastructure based on the peak needs of workloads. This guarantees the maximums are met, but can create a lot of waste. Pepperdata Capacity Optimizer uses machine learning to make thousands of decisions per second, analyzing and optimizing the resource usage of each node in real time to optimize the utilization of CPU, memory, and I/O resources on big data clusters. The net effect is that horizontal scaling is optimized and waste is eliminated. With automated tuning you can:
Pepperdata for Amazon EMR includes:
Automatically tune applications and infrastructure and recapture cloud resources. Optimize your cluster resources and run more applications.
Diagnose app performance issues faster and improve efficiency. Pinpoint straggling tasks or poor parallelization that impact runtime. Improve Spark app performance. Get job-specific recommendations, and set up alerts to avoid the risk of failure or missing SLAs.
Get full-stack observability of your infrastructure and resource utilization, performance recommendations, and custom alerts. Get historical cluster data including system demand, abusive users, and wasteful applications.
Gain access to Hive- and Impala-specific plan and execution information. Get quick root cause analysis with detailed visibility into query workloads — including delayed and most expensive queries as well as wasted CPU and memory queries.