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Software Functionality Revealed in Detail
We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.
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 warehouse data model


Best Practices for a Data Warehouse on Oracle Database 11g
Companies are recognizing the value of an enterprise data warehouse (EDW) that provides a single 360-degree view of the business. But to ensure that your EDW

warehouse data model  Resources Related to Data Warehouse : Data Warehouse (Wikipedia) Best Practices for a Data Warehouse on Oracle Database 11g Data Warehouse is also known as : Data Warehouse , Data Warehouse Architecture , Data Warehouse Concepts , Data Warehousing Information Center , Data Integration Paper , Data Warehouse Software , Data Warehousing Analysis , Data Warehouse Community , Data Warehouse Automation , Perspectives on Data Warehousing , Data Warehousing OLAP , Resource Data Warehouse , Land and Resource

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Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

Get free sample report
Compare Software Solutions

Visit the TEC store to compare leading software by functionality, so that you can make accurate and informed software purchasing decisions.

Compare Now

ERP for the Oil and Gas Industry (Upstream)

The model of ERP systems for the upstream oil and gas sector addresses the particularly specialized business model of this industry. It is common practice for companies, individuals, and government agencies to form partnerships to explore, develop, and share production of oil wells. More often than not, this is a short-term alliance rather than a long-term partnership, and business structuring must therefore be flexible at initial set-up and throughout the lifespan of the venture. This model of ERP systems includes criteria for financials, human resources, production data capture and reporting, maintenance management, and supply chain management (SCM) functionality. 

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Documents related to » warehouse data model

Ask the Experts Question: Do Organizations Really Need a Physical Data Warehouse Structure?


We recently had a question from one of our readers (through our Ask the Experts page) discussing QlikView’s approach to data collection. Reader’s Question “QlikView says its innovative way of collecting data and not needing a physical data warehouse (DW) structure is the right thing to do in DW/business intelligence (BI) solutions. Can one expect to build a sustainable / scalable corporate

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SAS/Warehouse 2.0 Goes Live


SAS Institute has announced the production availability of SAS/Warehouse Administrator software, Version 2.0. This new version provides IT the ability to proactively publish data warehouse information and track its usage, plus aggressively manage the process of change in the data warehouse.

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An Architect's Evaluation of Form and Function—the Dimensional Data Model


A detailed assessment and evaluation of data warehouse system functionality and how it applies to the dimensional data model using tools that the architect works with.

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Thinking Radically about Data Warehousing and Big Data: Interview with Roger Gaskell, CTO of Kognitio


Managing data—particularly in large numbers—still is and probably will be the number one priority for many organizations in the upcoming years. Many of the traditional ways to store and analyze large amounts of data are being replaced with new technologies and methodologies to manage the new volume, complexity, and analysis requirements. These include new ways of developing data warehousing, the

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2012 Business Data Loss Survey results


This report on the Cibecs and IDG Connect 2012 business data loss survey uncovers the latest statistics and trends around enterprise data protection. Download the full results now.

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Big Data Analytics: Profiling the Use of Analytical Platforms in User Organizations


While terabytes used to be synonymous with big data warehouses, now it’s petabytes, and the rate of growth in data volumes continues to escalate as organizations seek to store and analyze greater levels of transaction details, as well as Web- and machine-generated data, to gain a better understanding of customer behavior and drivers. This report examines the rise of "big data" and the use of analytics to mine that data.

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Agile Data Masking: Mitigate the Threat of Data Loss Prevention


You may not be as protected from data loss as you think. This infographic looks at some ways in which an enterprise's data can be compromised and vulnerable to security breaches and data loss, and shows how data masking can mean lower security risk and increased defense against data leaks.

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Considerations for Owning versus Outsourcing Data Center Physical Infrastructure


When faced with the decision of upgrading an existing data center, building new, or leasing space in a retail colocation data center, there are both quantitative and qualitative differences to consider. The 10-year TCO may favor upgrading or building over outsourcing; however, this paper demonstrates that the economics may be overwhelmed by a business’ sensitivity to cash flow, cash crossover point, deployment timeframe, data center life expectancy, regulatory requirements, and other strategic factors. This paper discusses how to assess these key factors to help make a sound decision.

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Backing up Data in the Private Cloud: Meeting the Challenges of Remote Data Protection - Requirements and Best Practices


This white paper describes some of the common challenges associated with protecting data on laptops and at home and remote offices and portrays proven solutions to the challenges of protecting distributed business data by establishing a private cloud/enterprise cloud. Learn which best practices can ensure business continuity throughout an organization with a distributed information technology (IT) infrastructure.

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Achieving a Successful Data Migration


The data migration phase can consume up to 40 percent of the budget for an application implementation or upgrade. Without separate metrics for migration, data migration problems can lead an organization to judge the entire project a failure, with the conclusion that the new package or upgrade is faulty--when in fact, the problem lies in the data migration process.

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