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Pepperdata

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  • Capacity Manager Q and A – How to Improve Productivity, Throughput, and Uptime
    Capacity Manager Q and A – How to Improve Productivity, Throughput, and Uptime Kirk Lewis, Field Engineer Recorded: Oct 10 2018 34 mins
    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
    Developer Q and A – Improve Your Application Performance and Efficiency Alex Pierce, Field Engineer Recorded: Sep 26 2018 21 mins
    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
    Four Ways Operators Can Fix Slowdowns and Improve Big Data Cluster Performance Kirk Lewis Recorded: Aug 22 2018 28 mins
    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
    How Operations Performance Management Informs APM to Help Big Data Developers Alex Pierce Recorded: Aug 8 2018 17 mins
    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
    Application Performance Management (APM) for Big Data Apps and Infrastructure Kirk Lewis, Pepperdata Field Engineer Recorded: Jul 18 2018 37 mins
    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
    Diagnosing Application Failures and Errors Kirk Lewis Recorded: May 16 2018 40 mins
    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
    Building a Big Data Stack on Kubernetes Sean Suchter Recorded: May 2 2018 48 mins
    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
    Total Performance Management (TPM) for Hadoop and Spark Alex Pierce Recorded: Apr 25 2018 23 mins
    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?
    Doesn't YARN Already Do This? Kirk Lewis Recorded: Apr 11 2018 33 mins
    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
    Make Spark Apps Go Fast! Fix Failures & Bottlenecks with Application Spotlight Vinod Nair Recorded: Apr 4 2018 43 mins
    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.

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