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Pepperdata Application Summary Page Overview

Find any application easily with a simple new application search capability. Intended for software engineers, developers, operators, architects and technical leads who develop Spark applications, Pepperdata has simplified the task of application performance management. Pepperdata Field Engineer Alex Pierce demonstrates how to identify bottlenecks and get recommendations and insights to improve the performance of your application in one place.
Recorded Dec 19 2017 22 mins
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Alex Pierce
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  • Operations Manager Q and A – Do More with Your Big Data Platform Oct 24 2018 6:00 pm UTC 60 mins
    Alex Pierce, Field Engineer
    Organizations are faced with countless obstacles to achieving big data success, including platform, application and user issues, as well as limited resources. This webinar will answer operational management questions around optimizing performance and maximizing capacity, such as “Who’s blowing up our cluster?, “How can I run more applications?” and more. You will learn from our expert, based on real-world deployments, how a complete APM solution provides:

    – Reduced mean time to problem resolution.
    – An accurate understanding of the most expensive users.
    – Improved platform throughput, uptime, efficiency and performance.
    – Reduced backlog.
    – And more.

    Presenter

    Alex Pierce joined Pepperdata in 2014. Previously, he worked as a senior solution architect at WanDisco. Before that, he was the senior solution architect at Red Hat. Alex has a strong background in system administration and big data.
  • Capacity Manager Q and A – How to Improve Productivity, Throughput, and Uptime Oct 10 2018 6:00 pm UTC 60 mins
    Kirk Lewis, Field Engineer
    There are numerous challenges to leveraging your big data infrastructure for optimal performance. This webinar answers operational management questions around optimizing performance and maximizing capacity, such as “Who’s blowing up our cluster?”, “How can I run more applications?” and more. You will learn from our expert, based on real-world deployments, how a complete APM solution delivers:

    – Improved throughput, uptime, efficiency and performance.
    – Accurate capacity planning.
    – Deploy capacity accurately for predictable performance.
    – Recapture wasted resources to maximize current infrastructure.

    Presenter

    Kirk joined Pepperdata in 2015. Previously, he was a Solutions Engineer at StackVelocity. Before that he was the lead technical architect for big data production platforms at American Express. Kirk has a strong background in big data.
  • Developer Q and A – Improve Your Application Performance and Efficiency Sep 26 2018 6:00 pm UTC 60 mins
    Alex Pierce, Field Engineer
    Developers are faced with specific challenges to big data success, including poor performance, unpredictable runtimes, and bottlenecks. This webinar will focus on answering questions you need answers to, like “Why is my job running so slow? and “How do I guarantee SLAs of my app?” Our expert will answer these questions and more, with insight from real-world deployments. Learn how to achieve:

    – Improved application performance and efficiency.
    – Reduced troubleshooting time.
    – Improved resource utilization.
    – Insight on cluster events impacting applications.
    – And more.

    Presenter

    Alex joined Pepperdata in 2014. Previously, he worked as a senior solution architect at WanDisco. Before that, he was the senior solution architect at Red Hat. Alex has a strong background in system administration and big data.
  • Four Ways Operators Can Fix Slowdowns and Improve Big Data Cluster Performance Recorded: Aug 22 2018 28 mins
    Kirk Lewis
    Despite tremendous progress, there are critically important areas, including multi-tenancy, performance optimization, and workflow monitoring where the DevOps team still need management help.

    In this webinar, presenter and Pepperdata Field Engineer, Kirk Lewis discusses why big data clusters slow down, how to fix them, and how to keep them running at an optimal level. In this online webinar followed by a live Q and A, Field Engineer Kirk Lewis discusses:

    • How Pepperdata Cluster Analyzer helps operators overcome Hadoop and Spark performance limitations by monitoring all facets of cluster performance in real time, including CPU, RAM, disk I/O, and network usage by user, job, and task.

    • How Pepperdata Capacity Optimizer increases capacity utilization by 30-50% without adding new hardware

    • How Pepperdata adaptively and automatically tunes the cluster based on real-time resource utilization with performance improvement results that cannot be achieved through manual tuning.
  • How Operations Performance Management Informs APM to Help Big Data Developers Recorded: Aug 8 2018 17 mins
    Alex Pierce
    Continually tuning your applications isn’t the best APM scenario for big data developers. This webinar discusses how big data operations performance management (OPM) provides the necessary context for more robust APM. A routine element of running a big big data platform is hardware. The OPM resource usage metrics that provide operators with clarity when something is not a clear cut bottleneck in the realm of CPU, memory, IO performance, are also extremely useful to developers as well, and can provide them with notifications and alerts for hardware related issues like network errors on a specific network interface.

    This webinar discusses how big data total performance management (TPM) combines operations performance management (OPM) and applications performance management (APM).

    Pepperdata® Application Spotlight is a self-service APM portal that provides developers with a consolidated view to improve troubleshooting and optimize application performance. Application Spotlight enables big data application developers to quickly and easily improve performance with more relevant application information, performance recommendations, insights, and calls to action, all in one place. In addition to helping them make their jobs go faster, Application Spotlight enables developers to be better tenants in multi-tenant clusters and shows them how to write optimal performing jobs and more efficiently use their queue and cluster resources with practical, innovative application performance management solutions.
  • Application Performance Management (APM) for Big Data Apps and Infrastructure Recorded: Jul 18 2018 37 mins
    Kirk Lewis, Pepperdata Field Engineer
    Pepperdata Application Spotlight analyzes all Hadoop and Spark jobs running on the cluster and provides developers with technical insights on how each job performed. Intended for software engineers, developers, and technical leads who develop Spark applications, this webinar demonstrates how Application Spotlight helps developers quickly improve application performance, reduce resource usage, and understand application failures.

    Learn how developers can:

    – Maximize performance, improve productivity, guarantee reliability, and improve ROI
    – Generate application-specific recommendations to improve application performance
    – Highlight applications that need attention
    – Automatically identify bottlenecks, and alert on duration, failure conditions, and resource usage
    – Search for any applications running on the cluster, compare current and previous runs
    – Visualize Spark applications and its stages for easy root cause failure analysis and performance tuning

    Presenter Bio: Field Engineer, Kirk Lewis

    Kirk Lewis joined the Pepperdata team in 2015. Previously, he was a Solutions Engineer at StackVelocity. Before that he was the lead technical architect for big data production platforms at American Express. Kirk has a strong background in big data.
  • Diagnosing Application Failures and Errors Recorded: May 16 2018 40 mins
    Kirk Lewis
    This webinar will present the results of analyzing many Hadoop and Spark jobs on many multi-tenant production clusters. We will cover common issues seen, the symptoms of those issues, and how to address them. We will discuss the Pepperdata APM solution and discuss best practices for diagnosing application failures and errors.

    Pepperdata has gathered trillions of performance data points on production clusters running Hadoop and Spark, covering a variety of industries, applications, and workload types. We will present key performance insights — best and worst practices, gotchas, and tuning recommendations — based on analyzing the behavior and performance of millions of applications.
  • Building a Big Data Stack on Kubernetes Recorded: May 2 2018 48 mins
    Sean Suchter
    There is growing interest in running Apache Spark natively on Kubernetes (see https://github.com/apache-spark-on-k8s/spark). Intended for software engineers, developers, architects and technical leads who develop Spark applications, this session will discuss how to build a big data stack on Kubernetes. In particular, Sean will demonstrate:

    –The official Apache Spark 2.3 Kubernetes integration
    –How Spark scheduler can still provide HDFS data locality on Kubernetes by discovering the mapping of Kubernetes containers to physical nodes to HDFS datanode daemons.
    –How you can provide Spark with the high availability of the critical HDFS namenode service when running HDFS in Kubernetes.
  • Total Performance Management (TPM) for Hadoop and Spark Recorded: Apr 25 2018 23 mins
    Alex Pierce
    Pepperdata makes Hadoop+YARN based systems better by providing total performance management (TPM) for big data. Total performance management is the combination of application performance management (APM) and operations performance management (OPM) in a single package so developers and operators can rely on the same underlying information to build and operate highly performant big data applications in multi-tenant clusters.

    For developers, the Application Spotlight self-service APM portal surfaces applications that need attention from a performance perspective. For these applications, Application Spotlight provides precise recommendations to improve performance, automatically identifies bottlenecks and makes it easy to analyze root cause of errors and failures.

    For operators, the Cluster Analyzer OPM solution makes it easy to identify applications and users causing issues on the platform, proactively alert on those issues, and improve cluster performance. We also have roll up reports for things like chargeback and capacity planning. The Capacity Optimizer add-on module automatically increases cluster throughput 30-50% by addressing some of the inefficiencies of how YARN does resource management today.

    Join us for this webinar presented by Alex Pierce and learn how we can bring performance management to your applications and your cluster.
  • Doesn't YARN Already Do This? Recorded: Apr 11 2018 33 mins
    Kirk Lewis
    This is a webinar that discusses the limitations of manually tuning Hadoop and how Pepperdata Improves YARN and the ResourceManager.

    Pepperdata makes Hadoop+YARN based systems better by providing total performance management (TPM) for big data. Total performance management is the combination of application performance management (APM) and operations performance management (OPM) in a single package so developers and operators can rely on the same underlying information to build and operate highly performant big data applications in multi-tenant clusters.

    For developers, the Application Spotlight self-service APM portal surfaces applications that need attention from a performance perspective. For these applications, Application Spotlight provides precise recommendations to improve performance, automatically identifies bottlenecks and makes it easy to analyze root cause of errors and failures.

    For operators, the Cluster Analyzer OPM solution makes it easy to identify applications and users causing issues on the platform, proactively alert on those issues, and improve cluster performance. We also have roll up reports for things like chargeback and capacity planning. The Capacity Optimizer add-on module automatically increases cluster throughput 30-50% by addressing some of the inefficiencies of how YARN does resource management today.

    Pepperdata solutions are certified for use with Cloudera, Hortonworks, and MapR. Sometimes we are asked, Doesn’t YARN already do this? The answer is that Pepperdata does not replace YARN or the ResourceManager, but can significantly augment its capabilities. This webinar discusses the limitations of manually tuning Hadoop and how Pepperdata improves YARN and the ResourceManager.
  • Make Spark Apps Go Fast! Fix Failures & Bottlenecks with Application Spotlight Recorded: Apr 4 2018 43 mins
    Vinod Nair
    Pepperdata Application Spotlight analyzes all Hadoop and Spark jobs running on the cluster and provides developers with technical insights on how each job performed. Application Spotlight provides relevant application information, insights, and calls to action, all in one place, so that developers can easily and quickly perform these tasks.

    In addition to making jobs go faster, Application Spotlight helps developers to be better tenants in multi-tenant clusters by showing them how to write optimal jobs and more efficiently use their queue and cluster resources with practical, innovative application performance management solutions. Application Spotlight enables developers to quickly understand performance impacts and get recommendations on how to better optimize their jobs.”

    Intended for software engineers, developers, and technical leads who develop Spark applications, this webinar demonstrates how Application Spotlight helps developers quickly improve application performance, reduce resource usage, and understand application failures. Learn how developers can:

    – Generate application-specific recommendations to improve application performance
    – Highlight applications that need attention
    – Automatically identify bottlenecks, and alert on duration, failure conditions, and resource usage.
    – Search for any applications running on the cluster, compare current and previous runs, and visualize Spark applications and its stages for easy root cause failure analysis and performance tuning.

    Presenter:

    Vinod Nair leads product management at Pepperdata. He brings more than 20 years of experience in engineering and product management to the job, with a special interest in distributed systems and Hadoop. He has worked in software for telecommunications, financial management for small business, and big data. Vinod’s approach to product management is deeply influenced by his success in applying Lean Startup principles and rapid iteration to product design and development.
  • Three Ways That Operators Can Fix Slowdowns and Improve Cluster Performance Recorded: Mar 21 2018 58 mins
    Kirk Lewis
    Despite tremendous progress, there remain critically important areas, including multi-tenancy, performance optimization, and workflow monitoring where the DevOps team still requires management help. In this webinar, presenter Kirk lewis discusses the ways that big data clusters slow down, how to fix them, and how to keep them running at an optimal level. He also presents an overview of Pepperdata operation performance management (OPM) solutions. In this online webinar followed by a live Q and A, Field Engineer Kirk Lewis discusses:

    • How Pepperdata Cluster Analyzer helps operators overcome Hadoop and Spark performance limitations by monitoring all facets of cluster performance in real time, including CPU, RAM, disk I/O, and network usage by user, job, and task.

    • How Pepperdata Capacity Optimizer increases capacity utilization by 30-50% without adding new hardware

    • How Pepperdata adaptively and automatically tunes the cluster based on real-time resource utilization with performance improvement results that cannot be achieved through manual tuning.

    Presenter Bio

    Kirk Lewis joined Pepperdata in 2015. Previously, he was a Solutions Engineer at StackVelocity. Before that he was the lead technical architect for big data production platforms at American Express. Kirk has a strong background in big data.
  • HDFS High Availability (HA) on Kubernetes Recorded: Mar 14 2018 31 mins
    Kimoon Kim
    Part of our Kubernetes, Lessons Learned Series, HDFS High Availability (HA) on Kubernetes is a webinar presentation intended for software engineers, developers, and technical leads who develop Spark applications and are interested in running Spark on Kubernetes while accessing HDFS data.

    Pepperdata has been exploring Kubernetes as potential Big Data platform with several other companies as part of a joint open source project. In this webinar, Kimoon Kim will show you how to:

    – Run Spark application natively on Kubernetes
    – Set up HDFS on Kubernetes in HA (High Availability) mode to ensure data durability

    Kimoon joined Pepperdata in 2013. Previously, he worked for the Google Search and Yahoo Search teams for many years. Kimoon has hands-on experience with large distributed systems processing massive data sets.
  • Spark Application Performance Management with Pepperdata Application Spotlight Recorded: Feb 21 2018 43 mins
    Vinod Nair
    Pepperdata Application Spotlight analyzes all Hadoop and Spark jobs running on the cluster and provides developers with technical insights on how each job performed. Intended for software engineers, developers, and technical leads who develop Spark applications, this webinar demonstrates how Application Spotlight helps developers quickly improve application performance, reduce resource usage, and understand application failures. Participate in this webinar and learn how developers can:

    –Identify the lines of code and the stages that cause performance issues related to CPU, memory, garbage collection, network, and disk I/O

    –Easily disambiguate resources used during parallel stages

    –Understand why run-time variations occur for the same application

    –Determine whether performance issues are due to the application or other workloads on the cluster

    –Receive actionable recommendations for tuning jobs

    –Validate tuning changes made to applications with a before and after comparison

    –View the highlights worst performing phases of jobs

    –Improve MapReduce and Spark developer productivity

    –Improve cluster efficiency based on clear recommendations on how to modify workloads and configurations


    Vinod Nair leads product management at Pepperdata. He brings more than 20 years of experience in engineering and product management to the job, with a special interest in distributed systems and Hadoop. He has worked in software for telecommunications, financial management for small business, and big data. Vinod’s approach to product management is deeply influenced by his success in applying Lean Startup principles and rapid iteration to product design and development.
  • Pepperdata Hadoop and Spark Performance Solutions for Dev and Ops Recorded: Feb 7 2018 58 mins
    Kirk Lewis
    Despite tremendous progress, there remain critically important areas, including multi-tenancy, performance optimization, and workflow monitoring where the DevOps team still needs management help. Pepperdata is the first company to integrate deep performance measurement and understanding into the DevOps process for Big Data applications. Pepperdata products enable developers to rapidly debug, optimize, and understand production applications while also enabling operators to diagnose and automatically solve performance problems in production multi-tenant clusters. Presented by Field Engineer Kirk Lewis, this webinar is an overview of Pepperdata products and services.

    In this online webinar followed by a live Q and A, Field Engineer Kirk Lewis will show you how to:

    • Reduce time to problem resolution using comprehensive and detailed performance data–Pepperdata Platform Spotlight helps operators overcome Hadoop and Spark performance limitations by monitoring all facets of cluster performance in real time, including CPU, RAM, disk I/O, and network usage by user, job, and task.

    • Increase capacity utilization by 30-50% without adding new hardware–Pepperdata adaptively and automatically tunes the cluster based on real-time resource utilization with performance improvement results that cannot be achieved through manual tuning.

    • Help developers understand and improve application performance–Pepperdata Application Spotlight enables developers to identify and fix application performance problems, excessive usage of resources, and application errors.
  • Building a Big Data Stack on Kubernetes Recorded: Jan 25 2018 51 mins
    Pepperdata Founder and CTO, Sean Suchter
    There is growing interest in running Apache Spark natively on Kubernetes (see https://github.com/apache-spark-on-k8s/spark).

    Intended for software engineers, developers, architects and technical leads who develop Spark applications, this session will discuss how to build a big data stack on Kubernetes. In particular, Sean will demonstrate:

    –The official Apache Spark 2.3 Kubernetes integration
    –How Spark scheduler can still provide HDFS data locality on Kubernetes by discovering the mapping of Kubernetes containers to physical nodes to HDFS datanode daemons.
    –How you can provide Spark with the high availability of the critical HDFS namenode service when running HDFS in Kubernetes.
  • Fix Spark Failures and Bottlenecks Faster and Easier Recorded: Jan 17 2018 49 mins
    Vinod Nair
    Intended for software engineers, developers, and technical leads who develop Spark applications, this webinar discusses the results of analyzing many Spark jobs on many multi-tenant production clusters, the common issues seen, the symptoms of those issues, and how developers can address them. Pepperdata has gathered trillions of performance data points on production clusters running Spark, covering a variety of industries, applications, and workload types.

    Presenter Vinod Nair will talks about key performance insights — best and worst practices, gotchas, and tuning recommendations — based on analyzing the behavior and performance of millions of Spark applications. In addition, Vinod will describe how we are turning these learnings into heuristics leveraged from the open source Dr. Elephant project.

    This webinar is followed by a live Q & A. A replay of this webinar will be available within 24 hours at https://www.pepperdata.com/resources/webinars/.
  • Pepperdata Application Summary Page Overview Recorded: Dec 19 2017 22 mins
    Alex Pierce
    Find any application easily with a simple new application search capability. Intended for software engineers, developers, operators, architects and technical leads who develop Spark applications, Pepperdata has simplified the task of application performance management. Pepperdata Field Engineer Alex Pierce demonstrates how to identify bottlenecks and get recommendations and insights to improve the performance of your application in one place.
  • Classifying Multi-Variate Time Series at Scale Recorded: Dec 7 2017 27 mins
    Ash Munshi
    Characterizing and understanding the runtime behavior of large-scale Big Data production systems is extremely important. Typical systems consist of hundreds to thousands of machines in a cluster with hundreds of terabytes of storage costing millions of dollars, solving problems that are business critical. By instrumenting each running process, and measuring their resource utilization including CPU, Memory, I/O, network etc., as time series it is possible to understand and characterize the workload on these massive clusters. Each time series is a series consisting of tens to tens of thousands of data points that must be ingested and then classified. At Pepperdata, our instrumentation of the clusters collects over three hundred metrics from each task every five seconds resulting in millions of data points per hour. At this scale the data are equivalent to the biggest IOT data sets in the world. Our objective is to classify the collection of time series into a set of classes that represent different work load types. Or phrased differently, our problem is essentially the problem of classifying multivariate time series.

    Intended for machine learning researchers and developers who use machine learning in their applications, Pepperdata CEO Ash Munshi presents a unique, off-the-shelf approach to classifying time series that achieves near best-in-class accuracy for univariate series and generalizes to multivariate time series.

    Before joining Pepperdata, Ash was executive chairman for Marianas Labs, a deep learning startup sold in December 2015. Prior to that he was CEO for Graphite Systems, a big data storage startup that was sold to EMC DSSD in August 2015. Munshi also served as CTO of Yahoo, as a CEO of both public and private companies, and is on the board of several technology startups.
  • Building a Big Data Stack on Kubernetes Recorded: Dec 6 2017 55 mins
    Sean Suchter
    There is growing interest in running Apache Spark natively on Kubernetes (see https://github.com/apache-spark-on-k8s/spark).

    Intended for software engineers, developers, architects and technical leads who develop Spark applications, this session will discuss how to build a big data stack on Kubernetes. In particular, it will show how Spark scheduler can still provide HDFS data locality on Kubernetes by discovering the mapping of Kubernetes containers to physical nodes to HDFS datanode daemons. You’ll also learn how you can provide Spark with the high availability of the critical HDFS namenode service when running HDFS in Kubernetes.
DevOps for Big Data
Pepperdata is the DevOps for Big Data company. Leading Enterprise companies depend on Pepperdata to manage and improve the performance of Hadoop and Spark. Developers and operators use Pepperdata products and services to diagnose and solve performance problems in production and increase cluster utilization. The Pepperdata product suite improves communication of performance issues between Dev and Ops, shortens time to production, and increases cluster ROI. Pepperdata products and services work with customer Big Data systems both on-premise and in the cloud

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