data warehouse

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Published By: Red Hat     Published Date: Jan 09, 2014
A large enterprise data warehouse company used Red Hat® CloudForms to create a private cloud that includes automated provisioning and self-service for developers and testers. This let them build, test, and release new product versions faster. Find out how in this case study.
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red hat, cloudforms, data, data warehouse, enterprise cloud, cloud management, productivity, private cloud, service delivery, lifecycle management, business technology
    
Red Hat
Published By: Juniper Networks     Published Date: Oct 19, 2015
Datacenters are the factories of the Internet age, just like warehouses, assembly lines, and machine shops were for the industrial age. Over the course of the past several years, riding the wave of modernization, datacenters have become the heart and soul of the financial industry, which each year invests over $480 billion in datacenter infrastructure of hardware, software, networks, and security and services.
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juniper, datacenter, threat, ciso, enterprise, data, customer
    
Juniper Networks
Published By: IBM     Published Date: Sep 22, 2011
This white paper highlights the performance and scalability potential of InfoSphere DataStage 8.1 based on a benchmark test simulating a data warehouse scenario. The benchmark is designed to use the profiled situation to provide insight about how InfoSphere DataStage addresses key questions customers frequently ask when designing their information integration architecture.
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ibm, infosphere, data performance and scalability, customers, datastage
    
IBM
Published By: Oracle PaaS/IaaS/Hardware     Published Date: Jul 25, 2017
"With the introduction of Oracle Database In-Memory and servers with the SPARC S7 and SPARC M7 processors Oracle delivers an architecture where analytics are run on live operational databases and not on data subsets in data warehouses. Decision-making is much faster and more accurate because the data is not a stale subset. And for those moving enterprise applications to the cloud, Real-time analytics of the SPARC S7 and SPARC M7 processors are available both in a private cloud on SPARC servers or in Oracle’s Public cloud in the SPARC cloud compute service. Moving to the Oracle Public Cloud does not compromise the benefits of SPARC solutions. Some examples of utilizing real time data for business decisions include: analysis of supply chain data for order fulfillment and supply optimization, analysis of customer purchase history for real time recommendations to customers using online purchasing systems, etc. "
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Oracle PaaS/IaaS/Hardware
Published By: Teradata     Published Date: Jan 28, 2015
Althrough Hadoop and related technologies have been with us for several years, most business intelligence (BI) professionals and their business counterparts still harbor a few misconceptions that need to be corrected about Hadoop and related technologies such as MapReduce. This webcast presents the 10 most common myths about Hadoop, then corrects them. The goal is to clarify what Hadoop is and does relative to BI, as well as in which business and technology situations Hadoop-based BI, data warehousing and analytics can be useful.
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teradata, business, intelligence, hadoop, data, integration, analytics, mapreduce, warehouse
    
Teradata
Published By: Teradata     Published Date: Jan 30, 2015
Our goal is to share best practices so you can understand how designing a data lake strategy can enhance and amplify existing investments and create new forms of business value.
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data lake, data warehouse, enterprise data, migration, enterprise use, data lake strategy, business value, data management, data center
    
Teradata
Published By: Teradata     Published Date: Jan 30, 2015
This TDWI Checklist Report discusses adjustments to DW architectures that real-world organizations are making today, so that Hadoop can help the DW environment satisfy new business requirements for big data management and big data analytics.
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data, data warehouse, hadoop, hadoop ecosystem, data architectures, data archiving, advanced analytics, data management, data center
    
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: Teradata     Published Date: Jan 30, 2015
Data from the Internet of Things makes an integrated data strategy more vital than ever.
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teradata, internet, things, iot, data, warehouse, analytics, patchwork, value, development, integration, supply chain, inventory, sales, market demand, channel partners, deployment, hadoop, aster, discovery
    
Teradata
Published By: RedPoint Global     Published Date: May 11, 2017
While they’re intensifying, business-data challenges aren’t new. Companies have tried several strategies in their attempt to harness the power of data in ways that are feasible and effective. The best data analyses and game-changing insights will never happen without the right data in the right place at the right time. That’s why data preparation is a non-negotiable must for any successful customer-engagement initiative. The fact is, you can’t simply load data from multiple sources and expect it to make sense. This white paper examines the shortcomings of traditional approaches such as data warehouses/data lakes and explores the power of connected data.
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customer engagement, marketing data, marketing data analytics, customer data platform
    
RedPoint Global
Published By: Pure Storage Australia     Published Date: Jun 23, 2017
As flash costs continue to drop and new, flash-driven designs help to magnify the compelling economic advantages AFAs offer relative to HDD-based designs, mainstream adoption of AFAs —first for primary storage workloads and then ultimately for secondary storage workloads — will accelerate. Well-designed AFAs that still leverage legacy interfaces like SAS will be able to meet many performance requirements over the next year or two. Those IT organisations that aim to best position themselves to handle future growth will want to look at next-generation AFA offerings, as the future is no longer flash-optimised architectures (implying that HDD design tenets had to be optimised around) — it is flash-driven architectures.
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cloud data, online marketing, customer acquisition, mobile marketing, social marketing, data warehouse, data storage, data collection
    
Pure Storage Australia
Published By: IBM     Published Date: May 17, 2016
Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
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ibm, big data, inline analytics, business analytics, roi
    
IBM
Published By: EMC Corporation     Published Date: Jul 07, 2013
Forward-looking enterprises know there's more to big data than strong and managing large volumes of information. Big data presents an opportunity to leverage analytics and experiment with all available data to derive value never before possible with traditional business intelligence and data warehouse platforms. Through a modern, big data platform that facilitates self-service and collaborative analytics across all data, organizations become more agile and are able to innovate in new ways.
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enterprises, storage, information management, technology, platform, big data analytics, emc, self service, business intelligence, security, knowledge management, enterprise applications, data management, business technology
    
EMC Corporation
Published By: CA Technologies EMEA     Published Date: Aug 03, 2017
Tenuto conto del fatto che la GDPR è stato annunciato formalmente solo di recente, si evidenzia un buon livello di consapevolezza tra i partecipanti. Una volta informati sul regolamento, l'88% degli intervistati ha dichiarato che la propria azienda deve affrontare difficoltà tecnologiche per la compliance alla GDPR. Il percorso verso la compliance è percepito come molto laborioso.
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generazione di dati sintetici, virtualizzazione di servizi, controllo e protezione dei database, masking dei dati, warehouse dei dati, ca technologies, gdpr
    
CA Technologies EMEA
Published By: Teradata     Published Date: Jan 20, 2015
This Neil Raden and Teradata webinar explores: The business values gained from an integrated view of SAP® and non-SAP® data; Existing solutions and challenges; Requirements for the optimal BI and analytics platform, and; A new solution that simplifies and enhances BI analytics for SAP® ERP data.
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data warehouse, teradata, business value, analytics platform, erp data, data management
    
Teradata
Published By: Teradata     Published Date: Jan 16, 2015
This Neil Raden paper describes the current need for data warehousing, why SAP® BW is an incomplete choice and how Teradata Analytics for SAP® Solutions is a superior option. Download now!
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teradata, sap solutions, data warehouse, extracted data, data management
    
Teradata
Published By: Teradata     Published Date: Jan 27, 2015
There is little question about the role that SAP® BW has historically played in the SAP® infrastructure. It has been a key element in unlocking SAP’s vast and complex store of operational data housed in their ERP applications. One can easily think of BW as the original Rosetta Stone for this data. For without it, end users and IT shops would have faced daunting coding tasks attempting to cull the proper elements out of the SAP® ERP code for reporting. However, BW and the associated tools around it were rudimentary to begin with, and have not advanced to where it meets the pressing demands of todays’ end users. The New Rosetta Stone 2.0 for SAP® ERP Data —And More. Download now!
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rosetta stone, data, erp, sap®, teradata, it management
    
Teradata
Published By: Teradata     Published Date: May 02, 2017
Read this article to discover the 4 things no data warehouse should be without.
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cloud data, cloud security, cloud management, storage resource, computing resources, data warehousing, data storage, cloud efficiency
    
Teradata
Published By: Teradata     Published Date: May 02, 2017
A Great Use of the Cloud: Recent trends in information management see companies shifting their focus to, or entertaining a notion for the first time of a cloud-based solution. In the past, the only clear choice for most organizations has been on-premises data—oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for all or some of a company’s analytical needs. This paper, written by McKnight Consulting analysts William McKnight and Jake Dolezal, describes two organizations with mature enterprise data warehouse capabilities, that have pivoted components of their architecture to accommodate the cloud.
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data projects, data volume, business data, cloud security, data storage, data management, cloud privacy, encryption, security integration
    
Teradata
Published By: Teradata     Published Date: May 02, 2017
Constellation Research presents case studies of two companies that cut costs, gained flexibility, and eased administration by adding cloud data warehouse capacity to their on-premises environment. Both of these deployments: • Solved problems and yielded expected and unexpected benefits • Required some adjustments to administrative processes and expectations • Led to hybrid architecture as being an invaluable aspect of their respective data warehousing strategies Download the case studies now to explore each company’s journey, understand recurring themes, and walk away with actionable insights.
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cloud data, online marketing, customer acquisition, mobile marketing, social marketing, data warehouse, data storage, data collection
    
Teradata
Published By: Teradata     Published Date: May 02, 2017
Should the data warehouse be deployed on the cloud? Read this IDC Research Spotlight to learn more.
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data warehouse, data storage, data management, data analytics, data preparation, data integration, system integration
    
Teradata
Published By: IBM     Published Date: Apr 19, 2016
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse. In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?
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ibm, ibm pure data system, big data, data analytics, analytics architecture, data warehouse, data management
    
IBM
Published By: IBM     Published Date: Jul 05, 2016
Cloud-based data warehousing as-a-service, built for analytics
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ibm, dashdb, data, analytics, data warehouse, cloud, analytics, business insights, knowledge management, enterprise applications, data management, business technology, data center, data science, data storage, data visualization
    
IBM
Published By: IBM     Published Date: Jul 05, 2016
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse. In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?
Tags : 
ibm, ibm pure data system, big data, data analytics, analytics architecture, data warehouse, knowledge management, data management, data center, analytics, data science, data storage, data visualization
    
IBM
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