hadoop

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By: NetApp     Published Date: Dec 13, 2013
FlexPod Select with Hadoop delivers enterprise class Hadoop with validated, pre-configured components for fast deployment, higher reliability and smoother integration with existing applications and infrastructure.  These technical reference architectures optimize storage, networking, and servers with Cloudera and Hortonworks distributions of Hadoop. Leverage FlexPod Select with Hadoop to help store, manage, process and perform advanced analytics on your multi-structured data.   Tuning parameters, optimization techniques among other Hadoop cluster guidelines  are provided.
Tags : flexpod with hadoop, enterprise data, storage infrastructure, massive amounts of data
     NetApp
By: NetApp     Published Date: Dec 13, 2013
Interested in running a Hadoop proof of concept on enterprise-class storage? Download this solutions guide to get a technical overview on building Hadoop on NetApp E-series storage. NetApp Open Solution for Hadoop delivers big analytics with preengineered, compatible, and supported solutions based on high-quality storage platforms so you reduce the cost, schedule, and risk of do-it-yourself systems and relieving the skills gap most organizations have with Hadoop. See how on going operational and maintenance costs can be reduced with a high available and scalable Hadoop solution.
Tags : open solutions, hadoop solutions guide
     NetApp
By: NetApp     Published Date: Dec 13, 2013
Learn why NetApp Open Solution for Hadoop is better than clusters built on commodity storage. This ESG lab report details the reasons why NetApp's use of direct attached storage for Hadoop improves performance, scalability and availability compared to typical internal hard drive Hadoop deployments.
Tags : netapp open solution for hadoop, direct attached storage
     NetApp
By: IBM     Published Date: May 20, 2015
IBM Platform Symphony - accelerate big data analytics – This demonstration will highlight the benefits and features of IBM Platform Symphony to accelerate big data analytics by maximizing distributed system performance, fully utilizing computing resources and effectively harnessing the power of Hadoop.
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     IBM
By: IBM     Published Date: May 20, 2015
Every day, the world creates 2.5 quintillion bytes of data and businesses are realizing tangible results from investments in big data analytics. IBM Spectrum Scale (GPFS) offers an enterprise class alternative to Hadoop Distributed File System (HDFS) for building big data platforms and provides a range of enterprise-class data management features. Spectrum Scale can be deployed independently or with IBM’s big data platform, consisting of IBM InfoSphere® BigInsights™ and IBM Platform™ Symphony. This document describes best practices for deploying Spectrum Scale in such environments to help ensure optimal performance and reliability.
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     IBM
By: Splice Machine     Published Date: Feb 13, 2014
Hadoop: Moving Beyond the Big Data Hype Let’s face it. There is a lot of hype surrounding Big Data and adoop, the defacto Big Data technology platform. Companies want to mine and act on massive data sets, or Big Data, to unlock insights that can help them improve operational efficiency, delight customers, and leapfrog their competition. Hadoop has become popular to store massive data sets because it can distribute them across inexpensive commodity servers. Hadoop is fundamentally a file system (HDFS or Hadoop Distributed File System) with a specialized programming model (MapReduce) to process the data in the files. Big Data has not lived up to expectations so far, partly because of limitations of Hadoop as a technology.
Tags : sql-on-hadoop® evaluation guide, splice machine, adoop
     Splice Machine
By: TIBCO     Published Date: Nov 09, 2015
As one of the most exciting and widely adopted open-source projects, Apache Spark in-memory clusters are driving new opportunities for application development as well as increased intake of IT infrastructure. Apache Spark is now the most active Apache project, with more than 600 contributions being made in the last 12 months by more than 200 organizations. A new survey conducted by Databricks—of 1,417 IT professionals working with Apache Spark finds that high-performance analytics applications that can work with big data are driving a large proportion of that demand. Apache Spark is now being used to aggregate multiple types of data in-memory versus only pulling data from Hadoop. For solution providers, the Apache Spark technology stack is a significant player because it’s one of the core technologies used to modernize data warehouses, a huge segment of the IT industry that accounts for multiple billions in revenue. Spark holds much promise for the future—with data lakes—a storage repo
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     TIBCO
By: RedPoint     Published Date: Sep 22, 2014
Enterprises can gain serious traction by taking advantage of the scalability, processing power and lower costs that Hadoop 2.0/YARN offers. YARN closes the functionality gap by opening Hadoop to mature enterprise-class data management capabilities. With a lot of data quality functionality left outside of Hadoop 1, and a lot of data inside HDFS originating outside the enterprise, the quality of the data residing in the Hadoop cluster is sometimes as stinky as elephant dung. Some of the topics discussed in this paper include: • The key features, benefits and limitations of Hadoop 1.0 • The benefit of performing data standardization, identity resolution, and master data management inside of Hadoop. • The transformative power of Hadoop 2.0 and its impact on the speed and cost of accessing, cleansing and delivering high-quality enterprise data. Download this illuminating white paper about what YARN really means to the world of big data management.
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     RedPoint
By: RedPoint     Published Date: Sep 22, 2014
The emergence of YARN for the Hadoop 2.0 platform has opened the door to new tools and applications that promise to allow more companies to reap the benefits of big data in ways never before possible with outcomes possibly never imagined. By separating the problem of cluster resource management from the data processing function, YARN offers a world beyond MapReduce: less encumbered by complex programming protocols, faster, and at a lower cost. Some of the topics discussed in this paper include: • Why is YARN important for realizing the power of Hadoop for data integration, quality and management? • Benchmark results of MapReduce vs. Pig vs. visual “data flow” design tools • The 3 key features of YARN that solve the complex problems that prohibit businesses from gaining maximum benefit from Hadoop. Download this paper to learn why the power of Hadoop 2.0 lies in enabling applications to run inside Hadoop, without the constraints of MapReduce.
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     RedPoint
By: RedPoint     Published Date: Nov 10, 2014
Adoption of Hadoop by data-driven organizations is exploding. Hadoop’s potential cost effectiveness and facility for accepting unstructured data is making it central to modern, “Big Data” architectures. The advancements in Hadoop 2.0 increase the technology’s promise to an even greater extent. But with these opportunities also come challenges and adoption hurdles that make getting the most out of Hadoop easier said than done. Read on as we review some Hadoop basics, highlight some of the adoption challenges that exist and explain how RedPoint Data Management for Hadoop helps organizations accelerate their work with Hadoop.
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     RedPoint
By: EMC     Published Date: Jun 13, 2016
A Data Lake can meet the storage needs of your Modern Data Center. Check out the Top 10 Reasons your organization should adopt scale-out data lake storage for Hadoop Analytics on EMC Isilon.
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     EMC
By: EMC     Published Date: Jun 13, 2016
IDC believes that EMC Isilon is indeed an easy to operate, highly scalable and efficient Enterprise Data Lake Platform. IDC validated that a shared storage model based on the Data Lake can in fact provide enterprise-grade service-levels while performing better than dedicated commodity off-the-shelf storage for Hadoop workloads.
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     EMC
By: IBM     Published Date: Nov 14, 2014
Every day, the world creates 2.5 quintillion bytes of data and businesses are realizing tangible results from investments in big data analytics. IBM Spectrum Scale (GPFS) offers an enterprise class alternative to Hadoop Distributed File System (HDFS) for building big data platforms and provides a range of enterprise-class data management features. Spectrum Scale can be deployed independently or with IBM’s big data platform, consisting of IBM InfoSphere® BigInsights™ and IBM Platform™ Symphony. This document describes best practices for deploying Spectrum Scale in such environments to help ensure optimal performance and reliability.
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     IBM
By: IBM     Published Date: Nov 14, 2014
Join Gartner, Inc. and IBM Platform Computing for an informative webinar where you will learn how to combine best of breed analytic solutions to provide a low latency, shared big data infrastructure. This helps government IT departments make faster decisions by analyzing massive amounts of data, improving security, detecting fraud, enabling faster decisions and saving cost by optimizing and sharing your existing infrastructure.
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     IBM
By: IBM     Published Date: Nov 14, 2014
Platform Symphony is an enterprise-class server platform that delivers low-latency, scaled-out MapReduce workloads. It supports multiple applications running concurrently so that organizations can increase utilization across all resources resulting in a high return on investment.
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     IBM
By: IBM     Published Date: Feb 13, 2015
IBM® has created a proprietary implementation of the open-source Hadoop MapReduce run-time that leverages the IBM Platform™ Symphony distributed computing middleware while maintaining application-level compatibility with Apache Hadoop.
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     IBM
By: IBM     Published Date: Feb 13, 2015
In the age of "big data," it's essential for any organization to know how to analyze and manage their ever increasing stores of information. Packed with everything you need to know about Hadoop analytics, this handy guide provides you with a solid understanding of the critical big data concepts and trends, and suggests ways for you to revolutionize your business operations through the implementation of cost-effective, high performance Hadoop technology.
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     IBM
By: MapR Technologies     Published Date: Sep 04, 2013
Enterprises are faced with new requirements for data. We now have big data that is different from the structured, cleansed corporate data repositories of the past. Before, we had to plan out structured queries. In the Hadoop world, we don’t have to sort data according to a predetermined schema when we collect it. We can store data as it arrives and decide what to do with it later. Today, there are different ways to analyze data collected in Hadoop—but which one is the best way forward?
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     MapR Technologies
By: Dell and Intel®     Published Date: Aug 24, 2015
Many enterprises are embracing Hadoop because of the unique business benefits it provides. But, until now, this rapidly evolving big data technology hadn’t always met enterprise security needs. In order to protect big data today, organizations must have solutions that address four key areas: authentication, authorization, audit and lineage, and compliant data protection.
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     Dell and Intel®
By: Dell and Intel®     Published Date: Aug 24, 2015
Business need: Merkle needed a scalable, cost-effective way to capture and analyze large amounts of structured and unstructured consumer data for use in developing better marketing campaigns for clients. Solution: The company deployed a Dell and HadoopTM cluster based on Dell and Intel® technologies to support a new big data insight solution that gives clients a unified view of customer data. Benefits: [bullets for the below points] • Partnership with Dell and Intel® leads to new big data solution • Cluster supports the Foundational Marketing Platform, a new data insight solution • Merkle can find patterns in big data and create analytical models that anticipate consumer behavior • Organization cuts costs by 60 percent and boosts processing speeds by 10 times • Solution provides scalability and enables innovation
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     Dell and Intel®
By: Dell and Intel®     Published Date: Aug 24, 2015
Organizations working at gathering insights from vast volumes of varied data types understand that they need more than traditional, structured systems, and tools. This paper discusses how the many Dell | Cloudera Hadoop solutions help organizations of all sizes, and with a variety of needs and use cases, tackle their big data requirements.
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     Dell and Intel®
By: Dell and Intel®     Published Date: Aug 24, 2015
This business-oriented white paper summarizes the wide-ranging benefits of the Hadoop platform, highlights common data processing use cases and explores examples of specific use cases in vertical industries. The information presented here draws on the collective experiences of three leaders in the use of Hadoop technologies—Dell and its partners Cloudera® and Intel®.
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     Dell and Intel®
By: Dell and Intel®     Published Date: Aug 24, 2015
To extract value from an ever-growing onslaught of data, your organization needs next-generation data management, integration, storage and processing systems that allow you to collect, manage, store and analyze data quickly, efficiently and cost-effectively. That’s the case with Dell| Cloudera® Apache™ Hadoop® solutions for big data. These solutions provide end-to-end scalable infrastructure, leveraging open source technologies, to allow you to simultaneously store and process large datasets in a distributed environment for data mining and analysis, on both structured and unstructured data, and to do it all in an affordable manner.
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     Dell and Intel®
By: Dell and Intel®     Published Date: Sep 06, 2015
In conclusion, the retail experience has changed dramatically in recent years as there has been a power shift over to consumers. Shoppers can easily find and compare products from an array of devices, even while walking through a store. They can share their opinions about retailers and products through social media and influence other prospective customers. To compete in this new multi-channel environment, we’ve seen in this guide how retailers have to adopt new and innovative strategies to attract and retain customers. Big data technologies, specifically Hadoop, enable retailers to connect with customers through multiple channels at an entirely new level by harnessing the vast volumes of new data available today. Hadoop helps retailers store, transform, integrate and analyze a wide variety of online and offline customer data—POS transactions, e-commerce transactions, clickstream data, email, social media, sensor data and call center records—all in one central repository. Retailers can
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     Dell and Intel®
By: GridGain     Published Date: Mar 10, 2015
Software as a Service (SaaS) is a software distribution model in which applications are hosted by a vendor or service provider and made available to customers over the Internet. Instead of companies installing software on their own servers, known as the on premises distribution model, application software providers host the software in the cloud and charge customers according to the time they spend using the software, or based on a monthly or annual fee. SaaS is becoming increasingly popular, and as the industry develops, more and more companies are dropping older business models in favor of this rapidly evolving methodology.
Tags : gridgain, saas, saas perfomance and scalability, in memory computing, data fabric, paas for saas, data grid, real-time streaming, hadoop
     GridGain
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