Autonomous Optimization and Full-Stack Observability Are Critical Requirements for Modern Big Data Performance Management

As big data stacks increase in scope and complexity, most data-driven organizations understand that automation and observability are necessary for modern real-time big data performance management. Without automation and observability, engineers and developers cannot optimize or ensure application and infrastructure performance, or keep cost under control. Pepperdata helps some of the most successful companies in the world manage their big data performance in the cloud and in the data center. These customers choose and trust Pepperdata because of three key product differentiators: autonomous optimization, full-stack observability, and cost optimization.

Autonomous Optimization

Many DevOps teams still manually tune their applications. The scale—thousands of applications per day and a growth rate of dozens of nodes per year—is too large and fast for manual efforts. Pepperdata Capacity Optimizer provides autonomous optimization that enables you to:

  • Reclaim resource waste with continuous tuning and automatic optimization.
  • Optimize Spark workloads with job-specific recommendations, insights, and alerts.
  • Get up to 50% throughput improvement to run more workloads.

Full-Stack Observability

Today’s world of accelerated cloud and microservices adoption has increased operational complexity, resulting in ephemeral environments with sometimes unpredictable behavior. Because of constant and dynamic workload changes, siloed solutions limit visibility and do not work across platforms. Today’s big data environments require observability to handle the complexity, volume, and speed to meet SLAs and business objectives.

Observability goes beyond traditional monitoring to explain system behavior over time and provide accurate operational insights. It also examines the sequence of a problem through monitoring, correlating the system data while providing automation based on machine learning (ML). Pepperdata Platform Spotlight and Application Spotlight provide big data observability, giving you actionable data about your applications and infrastructure. Understanding system behavior can transform your organization from being reactive to proactive to predictive. Observability helps organizations:

  • Get end-to-end system visibility.
  • Examine the sequence of a problem through monitoring, correlating system data while relying on ML-based automation.
  • Get accurate operational insights and job-specific recommendations.
  • Understand system behavior over time.

Cost Optimization

Optimizing operational costs is critical for your business. As data volumes increase so does complexity—as well as the costs of processing it. Whether you are running Apache Spark, Hive, Kubernetes, or Presto workloads on the cloud or on premises, Pepperdata can help your organization optimize operational costs. Pepperdata solutions help you:

  • Implement visibility and accountability. Use IT chargeback to easily view and charge business units for consumption by user, job, resource, or department.
  • Automatically optimize node performance and prevent application waste, enabling up to 50% more throughput. On top of Amazon EMR, Capacity Optimizer can reduce the number of cores by up to 63%, active nodes by up to 67%, and CPU idle time by up to 30%.
  • Improve application performance and reduce cost through automatic optimization and quicker troubleshooting.
  • Improve operating margins with data temperature by knowing which data should go to hot or cold cloud data storage. Manage all of your data and the escalating costs that come with it.
  • Understand your cloud costs before you migrate to the cloud.
  • Measure CPU and memory usage using CPU core-hours and memory byte-hours.


Learn how one of our customers automated their spark tuning and cut costs.

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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.