Doesn’t Yarn Already Do This? The Limitations of Manually Tuning Hadoop and How Pepperdata Improves YARN and the ResourceManager

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Pepperdata makes Hadoop+YARN based systems better by providing total performance management (TPM) for big data. TPM 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 require attention from a performance perspective. Application Spotlight provides precise recommendations to improve ...

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Total Performance Management – A Holistic View of Application and Platform Big Data Performance

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Total Performance Management has two components:

  • Application performance management (APM)
  • Operations performance management (OPM)

The reason why those two components application and platform need to be thought about together is because your application is a distributed application it relies on what your platform is able to do as well. Those two components are interlinked. With classic APM, you didn’t need to worry about the fact that you have other things are running on this application or that the ...

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Blog Tour of Application Summary

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This blog is the first in a series that introduces big data developers to Pepperdata Application Summary. Application Summary is the first in a series of Pepperdata guided application performance management (APM) user experiences. In these experiences, we solve a particular user problem (or use case) by providing all the relevant information, insights, and calls to action in one place so that the user can perform these tasks easily and quickly.

What is Application Summary?

Before I start the ...

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Four Big Data Predictions for 2018

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From our introduction of developer tools that provide Hadoop and Spark developers with easy-to-understand recommendations for improving job performance to working closely with our growing customer base of Big Data heavy hitters, 2017 was a good year for Pepperdata. We saw that Big Data is changing, with less emphasis on infrastructure and more emphasis on machine learning and applications. Spark continued to gain momentum, becoming the dominant framework for developing new Big Data applications. We think 2018 will ...

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Our Customers

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Last week marked three years for me at Pepperdata and it’s amazing the progress we’ve made as a company in the last few years. We’ve been fortunate to see more than 100% growth year-over-year and I thought in this post I would reflect on some of the reasons why customers work with Pepperdata.

Multi-tenancy is hard. All of our customers operate multi-tenant platforms, most at large scale, and that’s very challenging. Hundreds of users and tens of thousands of jobs per ...

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My Key Takeaways from Strata Data Conference NYC

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Thousands of developers, data engineers, data scientists, and business professionals attended Strata Data Conference last week in New York City. Events like these allow us to speak directly with customers, partners, and prospects on a range of issues: What Big Data projects are they working on? What are their biggest challenges and concerns? What trends are they seeing in the industry?

I wanted to share some of my observations and takeaways from last week based on the conference sessions I attended, ...

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New Program Serves Systems Integration Service Providers in Big Data and DevOps Market

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Earlier in the week, we announced the Pepperdata Partner Program to recognize qualified partners and help our customers accelerate and maximize the benefits of using the Pepperdata product suite. This strategic program will include partners who demonstrate a high level of expertise in successfully implementing and using the Pepperdata suite of operator and developer applications. Our new partners are industry leaders who understand Big Data and bring expertise to ensure customers can rapidly deploy Pepperdata to solve their challenges and ...

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Six Key Takeaways from Spark Summit and DataWorks Summit

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It’s been an extremely busy–but, very productive – couple of weeks. Pepperdata sponsored two critically important events attended by many of our clients and target customers.

Thousands of developers, data engineers, data scientist and business professionals attended each event. Spark Summit is dubbed the world’s largest event for the Apache Spark community, and DataWorks Summit is one of the industry’s premier big data community events. Combined, these events promote the opportunity to participate in hundreds of formal presentations as well as ...

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Solving Performance Bottlenecks for Developers—Introducing Pepperdata Code Analyzer for Spark

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Two years ago, IBM made a $300 million commitment to Spark. The investment shows how Spark has grown from its humble beginnings at UC Berkeley as an AMPLab project to a production-grade open source project. More importantly, it demonstrates an urgency around Spark as it sees continued enterprise adoption.

I witnessed Spark’s momentum firsthand as one of its earliest adopters. I first came across Spark back in 2011 as a founder at ...

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The Big Data Insiders: Q & A with Ian O’Connell

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As discussed in our last blog, Apache Spark has become the big thing in Big Data. As the largest open source community in Big Data, Spark enables flexible in-memory data processing and advanced analytics on the Hadoop platform. The roster of companies that have adopted Spark and also are project committers includes some of tech’s biggest names, such as Facebook, Netflix, Yahoo, IBM and Intel.

In the first installment of “The Big Data Insiders” — interviews with Big Data experts and ...

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