data warehouse

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Published By: SAS     Published Date: Sep 08, 2010
This paper describes five business analytics styles used today and the building blocks required in implementing these styles. It is important to consider which of these styles is valid for your organization now and into the future.
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sas, reporting, data warehouses, business activity monitoring, data integration, service oriented architecture, data warehousing
    
SAS
Published By: Oracle Corporation     Published Date: Mar 03, 2011
This white paper discusses how by designing these three corner stones correctly, you can seamlessly scale out your EDW without having to constantly tune or tweak the system.
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datawarehousing, system management, oracle, business intelligence
    
Oracle Corporation
Published By: Splunk     Published Date: Apr 16, 2012
Read how Splunk enabled WhitePages, the largest on-line directory of people and businesses, to protect the site's content and the brand by monitoring abusive site traffic and learn how Splunk's in-house search eliminated a data warehouse investment.
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splunk, whitepages, traffic, warehouse, data warehouse, directory, investment, threats data, analyzying, decision making, ime-series data, log management, log management software, manage logs, analyze logs, log analyzer, security log analysis, log management intelligence, log management compliance, compliance
    
Splunk
Published By: IBM     Published Date: Apr 16, 2014
Speed, simplicity and affordability: 3 capabilities businesses need from their warehousing environment. IBM DB2 with BLU Acceleration gives organizations a complete, multipurpose environment to rapidly distill insight from their data, make timely decisions and capitalize on opportunities
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ibm, data warehouse, warehouse environment, speed, simplicity, affordibility, db2 with blu, acceleration, data warehousing, data system, data storage, infrastructure, infrastructure management
    
IBM
Published By: IBM     Published Date: May 02, 2014
These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse.
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ibm, big data platform, architecting big data, analytics, intelligent business strategies, data complexity, data types, workload growth, workload complexity, big data analytic applications, operational decisions, multi-structured data, querying data, scalable data management, analytical ecosystem, hadoop solutions, it management, data management, data center
    
IBM
Published By: IBM     Published Date: Jul 14, 2015
This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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big data, data warehouse, data center, information governance, analytics, big data analytics, business management, data management
    
IBM
Published By: IBM     Published Date: Nov 09, 2015
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.
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ibm, data warehouse, big data, data management, business technology, data center, research
    
IBM
Published By: IBM     Published Date: Nov 09, 2015
IBM believes the Data Warehouse market continues to expand and adapt to address new requirements for user self-service, increased agility, requirements for new data types, lower cost solutions, adoption of open source, driving better business insight, and faster time to value.
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ibm, data, magic quadrant, data management, analytics, business technology
    
IBM
Published By: IBM     Published Date: Nov 16, 2015
As vendors continue to evolve their solutions to fit these changing requirements, IBM remains a leader in this Gartner Magic Quadrant.
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ibm, data warehouse, data management, analytics, gartner, business technology, data center, research
    
IBM
Published By: IBM     Published Date: Nov 16, 2015
The market offers an array of choices to organizations planning new data warehouses to manage large and varied data sets. Most vendors emphasize the speed of their products, but few address the real need: to increase speed efficiently, which reduces complexity and cost by simplifying data warehousing.
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ibm, organization, puredata, analytics, research, data warehouse, knowledge management, data management, business technology
    
IBM
Published By: IBM     Published Date: Apr 18, 2017
Are you looking to add new data domains or solve new business problems? IBM InfoSphere MDM comprises a complete MDM suite with multi-domain MDM capabilities and built-in domain patterns. IBM offers several editions of InfoSphere MDM, each designed to integrate with your existing architectures and business processes. You can choose the platform that best meets your immediate and long-term needs.
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cloud data, online marketing, customer acquisition, mobile marketing, social marketing, data warehouse, data storage, data collection
    
IBM
Published By: IBM     Published Date: Oct 01, 2013
In-depth Comparison of IBM Smart Analytics System 7700,Teradata Active Enterprise Data Warehouse and Oracle Exadata Database Machine.
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ibm, ibm smart analytics system, ibm smart analytics system 7700, it solutions, data warehouse, database, data management system, data mangementent, enterprise warehouse solutions, warehouse solutions, platforms, data management, business technology
    
IBM
Published By: IBM     Published Date: Jan 09, 2014
According to Dr. Barry Devlin of 9sight Consulting, the truth behind all the talk about big data and the possibilities it can offer is not hard to see, provided that organizations are willing to return to the principles of good data management processes.
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ibm, big data, 9sight consulting, data, it management, maximize business, deployment, business opportunities, big data usage, data warehouse, data center, business analytics, big data offerings, core business data, analytic data, puredata system, data virtualization, data integration, data types, data quality
    
IBM
Published By: IBM     Published Date: Mar 05, 2014
For many years, companies have been building data warehouses to analyze business activity and produce insights for decision makers to act on to improve business performance. These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse. Typically, a history of business activity is built up over a number of years allowing organizations to use business intelligence (BI) tools to analyze, compare and report on business performance over time. In addition, subsets of this data are often extracted from data warehouses into data marts that have been optimized for more detailed multi-dimensional analysis.
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ibm, big data, data, big data platform, analytics, data sources, data complexity, data volume, data generation, data management, storage, acceleration, business intelligence, data warehouse, analytical applications, data mining, data warehousing
    
IBM
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: Oct 06, 2014
Business Intelligence (BI) has become a mandatory part of every enterprise’s decision-making fabric. Unfortunately in many cases, with this rise in popularity, came a significant and disturbing complexity. Many BI environments began to have a myriad of moving parts: data warehouses and data marts deployed on multiple platforms and technologies – each requiring significant effort to ensure performance and support for the various needs and skill sets of the business resources using the environment. These convoluted systems became hard to manage or enhance with new requirements. To remain viable and sustainable, they must be simplified. Fortunately today, we have the ability to build simpler BI technical environments that still support the necessary business requirements but without the ensuing management complexity. This paper covers what is needed to simplify BI environments and the technologies that support this simplification.
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data warehouses, bi environments, bi technologies, faster deployments, knowledge management
    
IBM
Published By: IBM     Published Date: Jan 14, 2015
Decision makers need data and they need it now. As the pace of business continues to accelerate, organizations are leaning heavily on data warehouses to deliver analytical grist for the mill of daily decisions. This Research Report from Aberdeen Group examines the benefits of data warehouse solutions that offer rapid information delivery while minimizing complexity for users and IT.
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aberdeen group, data warehouse, data center, data management, analytic tools, collaboration, data trust, data analytics
    
IBM
Published By: IBM     Published Date: Apr 29, 2015
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.
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data warehouse, big data, unstructured data, implementations, data warehousing
    
IBM
Published By: IBM     Published Date: Jul 07, 2015
Learn about information integration and governance for data warehousing and big data and analytics.
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data warehouse, bad data, big data, mobility, compute-intensive apps, virtualization, cloud computing, scalable infrastructure, reliability, data management, data center
    
IBM
Published By: Calpont     Published Date: Mar 13, 2012
In this white paper you will find insight as to how the Calpont InfiniDB database performs far beyond the abilities of a row-based database.
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data warehouse, warehouse, benhcmark, infinidb, calpont, row-based, warehouse, mpp, parallel processing, columns, rows, column, row, data management
    
Calpont
Published By: IBM     Published Date: Jun 19, 2014
Download this ebook to learn the requirements for delivering trusted information to a modern data warehouse.
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ibm, big data, data warehousing, data, best practices, business intelligence
    
IBM
Published By: IBM     Published Date: Sep 15, 2014
Download this ebook to learn the requirements for delivering trusted information to a modern data warehouse and the guiding principles for trusted information in next generation data warehouse environments.
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ibm, data warehouse, data warehousing, hadoop, trusted data, data
    
IBM
Published By: IBM     Published Date: Jun 10, 2014
Download this short paper for an overview of the benchmark, the result and understand the importance to businesses deploying Hadoop into their data warehouse environments.
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ibm, big sql, hadoop, sql, data integration, data warehousing
    
IBM
Published By: IBM     Published Date: Nov 13, 2014
This paper focuses on the benefits of a queryable data store and big data technologies available to support data warehouse modernization. Read the paper to understand how data can be stored and optimized with Hadoop.
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ibm, ibm infosphere, big data, hadoop, big sql, data warehousing, analytical applications
    
IBM
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