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apache hadoop

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Published By: Teradata     Published Date: Jan 30, 2015
This report is about two of those architectures: Apache™ Hadoop® YARN and Teradata® Aster® Seamless Network Analytical Processing (SNAP) Framework™. In the report, each architecture is described; the use of each in a business problem is illustrated; and the results are compared.
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teradata, data, big, data, analytics. insights, solutions, business opportunities, challenges, technology, aster, snap, framework, apache, hadoop, yarn, architectures, data management, cloud computing
    
Teradata
Published By: Teradata     Published Date: Jan 30, 2015
It is hard for data and IT architects to understand what workloads should move, how to coordinate data movement and processing between systems, and how to integrate those systems to provide a broader and more flexible data platform. To better understand these topics, it is helpful to first understand what Hadoop and data warehouses were designed for and what uses were not originally intended as part of the design.
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teradata, data, big, data, analytics. insights, solutions, business opportunities, challenges, technology, framework, apache, hadoop, architecture, warehouse, optimization, security, scalability, consistency, flexibility, data management
    
Teradata
Published By: IBM     Published Date: Feb 22, 2016
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage.
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ibm, data, big data, integration, hadoop, enterprise applications, data management
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
This e-book highlights the benefits of Hadoop across several industries and explores how IBM® Biglnsights for Apache™ Hadoop® combines open source Hadoop with enterprise-grade management and analytic capabilities.
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ibm, analytics, big data, hadoop, enterprise, ibm biginsights, apache, enterprise management, enterprise applications, data management
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamlessly integrate with diverse technologies, from data marts to Apache Hadoop systems. And it must automatically discover, protect and monitor sensitive information as part of big data applications.
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IBM
Published By: IBM     Published Date: Apr 18, 2017
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage. An effective big data integration solution delivers simplicity, speed, scalability, functionality and governance to produce consumable data. To cut through this misinformation and develop an adoption plan for your Hadoop big data project, you must follow a best practices approach that takes into account emerging technologies, scalability requirements, and current resources and skill levels.
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data integration, data security, data optimization, data virtualization, database security, data migration, data assets, data delivery
    
IBM
Published By: IBM     Published Date: Jul 06, 2017
Known by its iconic yellow elephant, Apache Hadoop is purpose-built to help companies manage and extract insight from complex and diverse data environments. The scalability and flexibility of Hadoop might be appealing to the typical CIO but Aberdeen's research shows a variety of enticing business-friendly benefits.
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data management, yellow elephant, business benefits, information management
    
IBM
Published By: IBM     Published Date: Jul 05, 2016
This e-book highlights the benefits of Hadoop across several industries and explores how IBM® Biglnsights for Apache™ Hadoop® combines open source Hadoop with enterprise-grade management and analytic capabilities.
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ibm, analytics, big data, hadoop, enterprise, ibm biginsights, apache, enterprise management, knowledge management, enterprise applications, data management
    
IBM
Published By: Intel     Published Date: Jun 22, 2015
Until recently, we used the Intel® Distribution for Apache Hadoop Software (IDH) to support our original three business intelligence (BI) big data use cases, and it delivered results worth millions of dollars to Intel.
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Intel
Published By: Hortonworks     Published Date: Apr 05, 2016
Download this whitepaper to learn how Hortonworks Data Platform (HDP), built on Apache Hadoop, offers the ability to capture all structured and emerging types of data, keep it longer, and apply traditional and new analytic engines to drive business value, all in an economically feasible fashion. In particular, organizations are breathing new life into enterprise data warehouse (EDW)-centric data architectures by integrating HDP to take advantage of its capabilities and economics.
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Hortonworks
Published By: Hortonworks     Published Date: Apr 05, 2016
This white paper will help you evaluate your ability to protect your data in a Apache Hadoop ecosystem. Read on to learn ten signs that you might need to improve security and data governance in order to manage risk while getting more value out of your Apache Hadoop environment.
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Hortonworks
Published By: BlueData     Published Date: Mar 13, 2018
In a benchmark study, Intel compared the performance of Big Data workloads running on a bare-metal deployment versus running in Docker containers with the BlueData software platform. This landmark benchmark study used unmodified Apache Hadoop* workloads
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big data, big data analytics, hadoop, apache spark, docker
    
BlueData
Published By: Teradata     Published Date: Feb 26, 2013
This report explores the evolution of big data analytics and its maturity within the enterprise. It discusses the approaches and economics to using a Discovery platform and Apache Hadoop within the same unified analytical architecture.
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big data analytics, experiences with teradata, apache hadoop, analytics, discovery platform, apache hadoop, teradata, it management, data management, data center
    
Teradata
Published By: IBM     Published Date: May 02, 2014
This paper introduces you to Big SQL, answering many of the common questions that relational database management system (DBMS) users have about this IBM technology.
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ibm, big data, big sql, querying data, database management technology, apache hadoop, data administrators, infosphere, biginsights, industry-standard sql, management systems, database metadata, application programming interfaces, api, it management, best practices, change management
    
IBM
Published By: IBM     Published Date: Feb 03, 2016
Learn why advanced analytics tools are essential to sustain a competitive advantage. This white paper reveals seven strategic objectives that can be attained to their full potential only by employing predictive analytics.
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ibm, data management, apache, hadoop, analytics
    
IBM
Published By: IBM     Published Date: Feb 03, 2016
The more real-time and granular your data is, the more responsive and competitive your organization can become.
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ibm, data management, apache, hadoop, analytics
    
IBM
Published By: IBM     Published Date: Mar 05, 2014
If you specialize in relational database management technology, you’ve probably heard a lot about “big data” and the open source Apache Hadoop project. Perhaps you’ve also heard about IBM’s new Big SQL technology, which enables IBM® InfoSphere® BigInsights™ users to query Hadoop data using industry-standard SQL. Curious? This paper introduces you to Big SQL, answering many of the common questions that relational database management system (DBMS) users have about this IBM technology.
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ibm, big data, ibm big sql, sql, database management, database management technology, software, tables, queries, data platform, big sql architecture, programming language, relational database management system, rdbms, database development, analytical applications, business intelligence, data warehousing
    
IBM
Published By: WANdisco     Published Date: Oct 15, 2014
In this Gigaom Research webinar, the panel will discuss how the multi-cluster approach can be implemented in real systems, and whether and how it can be made to work. The panel will also talk about best practices for implementing the approach in organizations.
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wandisco, wan, wide area network, hadoop, clusters, clustering, load balancing, data, big data, data storage, storage, wide area networks, storage area networks
    
WANdisco
Published By: Altiscale     Published Date: Oct 19, 2015
In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.
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big data, analytics, nexgen, hadoop, apache, networking
    
Altiscale
Published By: IBM     Published Date: Apr 29, 2015
IBM InfoSphere BigInsights for Hadoop enables organizations to efficiently manage and mine large volumes of diverse data for valuable insights. IBM builds on a 100% Apache Hadoop foundation with common tools such as spreadsheets, R analytics and SQL access for greater usability.
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bigsheets, data management, business intelligence, workload optimization, data, sql, network management, network performance, network provisioning, database development, productivity
    
IBM
Published By: MapR Technologies     Published Date: Dec 12, 2013
Evaluator Group looks at what will make Hadoop an enterprise data center-grade analytics platform.
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big data, big data analytics, hadoop, apache hadoop, structured data, unstructured data, business analytics, metadata, analytics, mapreduce, data, data center, mapr
    
MapR Technologies
Published By: MapR Technologies     Published Date: Dec 12, 2013
When used effectively, Hadoop can deliver unparalleled value in revealing new analytics-driven revenue streams, improving customer acquisition and retention, as well as increasing operational efficiencies. The Hadoop Buyer's Guide is an invaluable resource for those investigating or evaluating Hadoop---from understanding how Hadoop can solve your data challenges, to what to look for when selecting a solution, to comparing vendors, and preparing for implementation and future success. Download the guide, and get everything you need to know about choosing the right Hadoop distribution for your business success.
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big data, big data analytics, hadoop, apache hadoop, structured data, unstructured data, business analytics, metadata, analytics, mapreduce, data, data center, mapr
    
MapR Technologies
Published By: MapR Technologies     Published Date: Dec 12, 2013
This independent whitepaper from the Kusnetzky Group Analyst describes the promise and challenges surrounding Big Data. It also validates the M7 solution from MapR, which simplifies big data management by consolidating disparate solutions into a single, enterprise-ready platform.
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big data, big data analytics, hadoop, apache hadoop, structured data, unstructured data, business analytics, metadata, analytics, mapreduce, data, data center, mapr, analytical applications
    
MapR Technologies
Published By: MapR Technologies     Published Date: Jan 08, 2014
Forrester Research shares seven architectural qualities for evaluating Big Data production platforms. In this webinar guest speaker Mike Gualtieri, Principal Analyst at Forrester, along with experts from MapR and Cisco, will present the following: • The 7 architectural qualities for productionizing Hadoop successfully • Architectural best practices for Big Data applications • The benefits of planning for scale • How Cisco IT is using best practices for their Big Data applications Speakers • Mike Gualtieri, Principal Analyst at Forrester Research • Jack Norris, Chief Marketing Officer at MapR Technologies • Andrew Blaisdell, Product Marketing Manager at Cisco • Sudharshan Seerapu, IT Engineer at Cisco
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big data, big data analytics, hadoop, apache hadoop, structured data, unstructured data, business analytics, metadata, analytics, mapreduce, data, data center, mapr
    
MapR Technologies
Published By: MapR Technologies     Published Date: Jan 03, 2014
As the demand for Big Data analytics mushrooms, IT decision-makers must prepare for the widespread deployment of Hadoop. This Technical Insight Paper from the Evaluator Group outlines the key requirements that must be met to make Hadoop enterprise data center ready.
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big data, big data analytics, hadoop, apache hadoop, structured data, unstructured data, business analytics, metadata, analytics, mapreduce, data, data center, mapr
    
MapR Technologies
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