Collateral Downloads

Product and Solutions

Application Spotlight Data Sheet (PDF)
The Application Spotlight Self-Service Big Data APM Portal gives you a 360° view of all your applications in realtime—in the context of the entire big data Hadoop or Spark cluster—in one place. It provides automatic tuning for applications, delivers job-specific recommendations and enables you to set up alerts on specific behaviors and outcomes to avoid the risk of failure. Improves application runtime, predictability, performance and efficiency.

Platform Spotlight Data Sheet (PDF)
Platform Spotlight provides you with a 360° view of your entire cluster in real-time to quickly diagnose performance issues and make resource decisions. It automatically tunes clusters to recapture wasted capacity, allows you to create alerts, and provides recommendations to rightsize containers, queues and other resources.

Products and Solutions Overview (PDF)
This document provides a technical overview of Pepperdata products, solutions, and services. Pepperdata is the APM leader for Big Data Success. We deliver proven products, operational experience, and deep expertise for customers. With the level of data that we collect and our extensive global enterprise experience, Pepperdata is the choice for organizations looking to get optimal performance and more value from their Big Data investments. 

Case Studies

Clearsense Case Study (PDF) 
Hospitals, physician groups, insurance providers, and research organizations rely on the Clearsense Platform to support life-saving applications, such as predictive algorithms for cardiac arrest and sepsis that notify caregivers and healthcare providers that such an event may occur. Other algorithms help hospital clients reduce costs and improve supply chain management. To ensure 99.999% uptime for these critical applications, Clearsense needed a scalable, reliable and comprehensive big data APM solution.

Rubicon Project Case Study (PDF)
Rubicon Project needed a robust monitoring and metrics solution that could provide them with granular insight on what was happening in their cluster. They knew that they could manage their clusters more efficiently and were considering a number of open-source and enterprise solutions.

Philips Wellcentive Case Study (PDF)
Philips Wellcentive processes over 35 million patients and three billion medical records daily. Their cluster became unpredictable when jobs and SLAs failed due to a critical performance issue. Despite engaging third-party support, the Hadoop technical team spent six months diagnosing the problem without success. Existing monitoring solutions didn’t provide the data to pinpoint the problem. As jobs failed, resolving the issue was the top priority.

Chartboost Case Study (PDF)
Chartboost faced managing explosive data growth. They had no way to effectively monitor and troubleshoot, or view granular health and status metrics for all of the clusters from a single, unified dashboard. Chartboost also had no way to effectively manage a multi-tenant cluster and ensure that high-priority jobs completed on time.

Comcast Case Study (Video) 
Mike Fagan from Comcast’s Big Data team talks about how his organization manages their multi-tenant data lakes using tools like Pepperdata and the challenges and best practices when it comes to governance in a multi-tenant Hadoop environment.

Industry Insights

Hadoop and Spark for the Enterprise: Ensuring Quality of Service in Multi-Tenant Environments
Learn how Pepperdata helps ensure Quality of Service in multi-tenant environments by guaranteeing service levels for high priority jobs and ensuring cluster hardware resources are used to maximum potential, to maximize throughput and hardware usage.

The Hadoop Performance Myth (PDF)
Deploying Hadoop involves many choices that can make or break your Hadoop initiative. Find out what successful Hadoop deployments have in common.

Data Analytics with Hadoop: A Practical Guide to Understanding Data Science and Analytics (PDF)
Learn how Pepperdata helps ensure Quality of Service in multi-tenant environments by guaranteeing service levels for high priority jobs and ensuring cluster hardware resources are used to maximum potential, to maximize throughput and hardware usage.

Best Practices for Setting up and Maintaining Reliable Hadoop Clusters (PDF)
Unlock the power of your data by learning how to ensure a successful start to your big data project. We’ll dive into tried and true process for how to build, maintain, and manage successful Hadoop deployments. This guide will show you how.

Analyst Paper: Pepperdata Enables Performance QoS for Hadoop (PDF)
Taneja Group analyst firm comments on the challenges for Enterprises looking to deploy Hadoop, and how Pepperdata helps organizations enforce SLAs and bridge the Hadoop skills gap with its automated cluster performance management.

Yarn: The Definitive Guide (PDF)
This is an excerpt from the O’Reilly book titled “Hadoop: The Definitive Guide.” This chapter, focused exclusively on YARN, will provide a deep dive into the workings of Hadoop schedulers, a comparison of the tools on the market, and best practices for building your own YARN applications.

High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (PDF)
This exclusive chapter from O’Reilly book “High Performance Spark” introduces Spark’s place in the big data ecosystem, and helps you understand how Spark programs are executed, its model for parallel computing, and improve your general understanding of this open source technology.

Data Analytics with Hadoop: A Practical Guide to Understanding Data Science and Analytics (PDF)
These 2 chapters from O’Reilly book “Data Analytics with Hadoop” focuses on the operational piece of how Hadoop works and how to execute workloads related to data analytics. Great for anyone involved in Hadoop operations and/or analytics with Hadoop.