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Improve Amazon EMR Performance up to 4X

Are you currently running Amazon EMR but lacking the visibility and measurement of how your cluster is performing? Pepperdata for Amazon EMR enables users of Amazon Elastic MapReduce to run jobs up to four times faster and simultaneously reduce costs. Users can see over 300 metrics even after the cluster has been terminated, so users have a historical view of performance.

Register for our webinar to learn how Amazon EMR can help streamline your big data projects, and how Pepperdata can help you get the most value from your investment.
Recorded Oct 13 2016 36 mins
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Presented by
Vinod Nair, Product Manager at Pepperdata
Presentation preview: Improve Amazon EMR Performance up to 4X

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  • Production Spark Series Part 4: Spark Streaming Delivers Critical Patient Care Jun 22 2017 6:00 pm UTC 60 mins
    Charles Boicey, Chief Innovation Officer, Clearsense
    Clearsense is a pioneer in healthcare data science solutions using Spark Streaming to provide real time updates to health care providers for critical health care needs. Clinicians are enabled to make timely decisions from the assessment of a patient's risk for Code Blue, Sepsis and other conditions based on the analysis of information gathered from streaming physiological monitoring along with streaming diagnostic data and the patient historical record. Additionally this technology is used to monitor operational and financial process for efficiency and cost savings. This talk discusses the architecture needed and the challenges associated with providing real time SLAs along with 100% uptime expectations in a multi-tenant Hadoop cluster.
  • HDFS on Kubernetes: Lessons Learned Jun 1 2017 6:00 pm UTC 60 mins
    Kimoon Kim, Engineer, Pepperdata
    There is growing interest in running Spark natively on Kubernetes (see https://github.com/apache-spark-on-k8s/spark). Spark applications often access data in HDFS, and Spark supports HDFS locality by scheduling tasks on nodes that have the task input data on their local disks. When running Spark on Kubernetes, if the HDFS daemons run outside Kubernetes, applications will slow down while accessing the data remotely.

    In this webinar, we will demonstrate how to run HDFS inside Kubernetes to speed up Spark. In particular, we will show:

    - 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 one can provide Spark with the high availability of the critical HDFS namenode service when running HDFS in Kubernetes.
  • Production Spark Series Part 3: Tuning Apache Spark Jobs May 30 2017 6:00 pm UTC 60 mins
    Simon King, Engineer, Pepperdata
    A Spark Application that worked well in a development environment or with sample data may not behave as expected when run against a much larger dataset in a production environment. Pepperdata Application Profiler, based on open source Dr Elephant, can help you tune you Spark Application based on current dataset characteristics and cluster execution environment. Application Profiler uses a set of heuristics to provide actionable recommendations to help you quickly tune your applications.

    Occasionally an application will fail (or execute too slowly) due to circumstances outside your control: a busy cluster, another misbehaving YARN application, bad luck, or bad "cluster weather". We'll discuss ways to use Pepperdata's Cluster Analyzer to quickly determine when an application failure may not be your fault and how to diagnose and fix symptoms that you can affect.
  • Production Spark Series Part 2: Connecting Your Code to Spark Internals May 9 2017 6:00 pm UTC 60 mins
    Sean Suchter, CTO/Co-Founder, Pepperdata
    Spark is a dynamic execution engine that can take relatively simple Scala code and create complex and optimized execution plans. In this talk, we will describe how user code translates into Spark drivers, executors, stages, tasks, transformations, and shuffles. We will describe how this is critical to the design of Spark and how this tight interplay allows very efficient execution. Users and operators who are aware of the concepts will become more effective at their interactions with Spark.
  • Big Data for Big Data: Machine Learning Models of Hadoop Cluster Behavior Recorded: Apr 10 2017 37 mins
    Sean Suchter, CTO/Co-Founder, Pepperdata and Shekhar Gupta, Software Engineer, Pepperdata
    Learn how to use machine learning to improve cluster performance.

    This talk describes the use of very fine-grained performance data from many Hadoop clusters to build a model predicting excessive swapping events.

    Performance of batch processing systems such as YARN is generally determined by the throughput, which measures the amount of workload (tasks) completed in a given time window. For a given cluster size, the throughput can be increased by running as much workload as possible on each host, to utilize all the free resources available on host. Because each node is running a complex combination of different tasks/containers, the performance characteristics of the cluster are dynamically changing. As a result, there is always a danger of overutilizing host memory, which can result into extreme swapping or thrashing. The impacts of thrashing can be very severe; it can actually reduce the throughput instead of increasing it.

    By using very fine-grained (5 second) data from many production clusters running very different workloads, we have trained a generalized model that very rapidly detects the onset of thrashing, within seconds from the first symptom. This detection has proven fast enough to enable effective mitigation of the negative symptom of thrashing, allowing the hosts to continuously provide high throughput.

    To build this system we used hand-labeling of bad events combined with large scale data processing using Hadoop, HBase, Spark, and iPython for experimentation. We will discuss the methods used as well as the novel findings about Big Data cluster performance.
  • Production Spark Webinar Series - Part 1: Best Practices for Spark in Production Recorded: Mar 7 2017 59 mins
    Chad Carson, Co-Founder and Ed Colonna, VP of Marketing
    Join us for our Part 1 of our Production Spark Webinar Series. This first installment gathers Spark experts and practitioners from varying backgrounds to discuss the top trends, challenges and use cases for production Spark applications. Our expert panel will discuss several key considerations when running Spark in production and take questions directly from the audience.

    Our distinguished panel of industry experts is as follows:

    Dr. Babak Behzad, Senior Software Engineer, SAP/Altiscale
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    Richard Williamson, Principal Engineer, Silicon Valley Data Science
    Andrew Ray, Principal Data Engineer, Silicon Valley Data Science
    Sean Suchter, CTO and Co-Founder, Pepperdata
  • Philips Wellcentive Cuts Hadoop Troubleshooting from Months to Hours Recorded: Dec 6 2016 48 mins
    Geovanie Marquez, Hadoop Architect at Philips Wellcentive
    Philips Wellcentive, a SaaS health management and data analytics company, relies on a nightly Mapreduce job to process and analyze data for their entire patient population; from birth to current day. It looks at their entire patient population to assess a number of different characteristics and powers the analytics that physician organizations need to deliver better services. When this job began to fail repeatedly, the Hadoop team spent months trying to identify the root cause using existing monitoring tools, but were unable to come up with an explanation for the job failures and slowdowns.

    Join our webinar to hear more about why existing Hadoop monitoring tools were insufficient to diagnose the root cause of Philips Wellcentive’s problems and how Pepperdata helped them to significantly improve their Big Data operations. The webinar will cover the different approaches that Philips Wellcentive took to rectify their missed SLAs, and how Pepperdata ultimately helped them quickly troubleshoot their performance problems and ensure their jobs complete on time.
  • Effectively Manage Multi-tenant Hadoop for the Enterprise Recorded: Nov 14 2016 39 mins
    Sean Suchter, CTO of Pepperdata
    As the Hadoop market matures and new applications and use cases for Big Data emerge, organizations are dealing with more complex environments than ever before. In days past, deployments often focused on single, batch-oriented workloads, and if you wanted to run multiple workloads at the same time, you needed to split your clusters. With Hadoop 2 and YARN, organizations are able to run multiple workloads on the same cluster. But, in multi-tenant environments, resource contention can become a daily problem and low-priority, ad-hoc jobs can sometimes monopolize hardware resource that is needed for high-priority workloads.

    Pepperdata is the first and only software that guarantees service levels in multi-tenant Hadoop environments. We have helped dozens of companies of all industries and all sizes to effectively manage and scale their multi-tenant environments, guaranteeing service levels and improving overall cluster performance.

    Join us for this webcast to hear best practices for running multi-tenant environments and how you can improve visibility, performance, and overall management of your big data environment.
  • Improve Amazon EMR Performance up to 4X Recorded: Oct 13 2016 36 mins
    Vinod Nair, Product Manager at Pepperdata
    Are you currently running Amazon EMR but lacking the visibility and measurement of how your cluster is performing? Pepperdata for Amazon EMR enables users of Amazon Elastic MapReduce to run jobs up to four times faster and simultaneously reduce costs. Users can see over 300 metrics even after the cluster has been terminated, so users have a historical view of performance.

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    Sean Suchter, CEO of Pepperdata and Dez Blanchfield, Data Scientist at the Bloor Group
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    Sean Suchter, CEO and co-founder of Pepperdata
    Most Hadoop admins have learned a set of “best practices” to address and mitigate this performance dilemma, such as manual tuning, cluster isolation, or adding new hardware, but most of these remedies are not sustainable long-term solutions. Join us for this webcast as we survey some of these “best practices” and offer up some new ways to address the performance gap. We’ll also tell you the warning signs to look out for, so you can assess the health and production readiness of your cluster.

    In this webcast, we’ll examine:

    the reality of what the current tools in the ecosystem do before and after a job has run,
    the most common “best practice” approaches to improve performance and the positive and negative outcomes of each, and
    a new approach to performance gains-how to use software to fill the gap of human capability.
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    Sean Suchter, Pepperdata Co-founder and CEO and Mike Matchett, Sr. Analyst at Taneja Group
    Come join us as we learn how to tackle and manage big data application performance. First, Taneja Group Sr. Analyst Mike Matchett will present his take on how enterprise IT is now being challenged to support big data applications in real production environments. He'll discuss why too many enterprises haven't been as successful as they should in taking advantage of their big data opportunities - in many cases losing out to competitors. He'll explore what agile IT/devops really needs to do to not only effectively host, but deliver top-notch, consistent big data performance with the smallest infrastructure cost.
    Then Sean Suchter, co-founder and CEO at Pepperdata will present their compelling approach to solving big data cluster performance challenges. He'll demonstrate how Pepperdata's dynamic run-time optimizations can guarantee consistent performance SLA's in a shared multi-tenant Hadoop cluster. Because Pepperdata delivers detailed visibility into Hadoop cluster activity , the software becomes invaluable for cluster troubleshooting, reporting/chargeback, capacity planning, and other management and optimization requirements. With Pepperdata, IT can now effectively, efficiently, and reliably support all the business-empowering big data applications of an organization. This webcast will be 45 minutes with time reserved for Q+A.
  • Real-time Hadoop and IoT Recorded: Oct 29 2015 53 mins
    Sean Suchter, Pepperdata and Bill Peterson, MapR
    When your business requires information be available in realtime, you can't rely on self-tuning to get you the performance you need. Hadoop addresses a lot of big data challenges, but it can't always guarantee that your priority jobs will complete on time.

    Join Pepperdata and MapR for this webcast to learn how you can use technology to go beyond the capabilities of human interaction. Hear REAL LIFE USE CASES of customers who have leveraged the right tools to address the volumes, decisions, and interactions required of IoT.
  • Big Data in Healthcare: How to accelerate your Hadoop & big data operations Recorded: Aug 20 2015 54 mins
    Sean Suchter, CEO & Co-founder of Pepperdata. Charles Boicey, Clinical & Information Technology Expert
    Attend our webinar to hear experts in the field of Big Data and Healthcare discuss the emergence of Hadoop and other Big Data technologies and how to make sure you're taking full advantage. LIVE QUESTIONS ARE ENCOURAGED. Topics we will cover:
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    - What tools & skill sets do you need once you are up and running
    - How to enforce SLAs in production to make sure your critical tasks are always completed

    We will have an active Q&A session and encourage questions from the audience - join us to guide the discussion!
  • Real-time Cluster Optimization for Hadoop and more Recorded: Jul 14 2015 39 mins
    Brock Alston, Pepperdata and Alex Pierce, Pepperdata
    You've deployed Hadoop. But now what? Are you spending a lot of time putting out fires on your production cluster? Are you able to identify the root cause of issues? Are your users complaining about jobs running more slowly or experiencing resource contention?

    This webcast will talk about the most common challenges for Enterprises running Hadoop in production, and will show you how you can use simple visibility and management tools to get:

    – Fine-grained visibility in your production cluster
    – Enforced SLA’s and enabled multi-tenancy
    – Increased throughput between 30 and 50%
The Big Data Performance Company
Pepperdata is the Big Data performance company. Leading Enterprise companies use Pepperdata products and services to manage and improve the performance of Hadoop and Spark. The Pepperdata product suite enables customers to troubleshoot performance problems in production, increase cluster utilization, and enforce policies to support multi-tenancy. Pepperdata products and services work with customer Big Data systems both on-premise and in the cloud.

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  • Title: Improve Amazon EMR Performance up to 4X
  • Live at: Oct 13 2016 6:00 pm
  • Presented by: Vinod Nair, Product Manager at Pepperdata
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