Advanced ETL with Pentaho Data Integration

When evaluating a data integration tool, you need to ask whether it handles dimension and fact transformations in a robust manner. You also need to determine how it handles error processing, and captures changes to dimensional data across time. Does it include reusable objects? What late-arriving fact functionality does it provide? And which data stores does it actually connect to?

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How to Use Projects to Master Asset Management

  • Source: IFS
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  • Published:
IFS co-founder Ulf Stern and senior advisor Anders Wilhelmsson discuss how project management functionality that is tightly integrated with enterprise asset management (EAM) and asset lifecycle management (ALM) tools is essential to executives of asset-intensive industries. Lacking this integration, valuable asset data is lost and asset utilization cannot be maximized. Read More

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TEC 2012 Business Intelligence and Data Management Buyer's Guide

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The advent of new computing models and innovative technologies is reshaping the business intelligence (BI) landscape. Read this buyer’s guide to get the latest on major changes in the BI and data management space and know the important factors to consider when looking for a BI solution. You’ll also get detailed product comparisons, a comprehensive vendor directory, customer success stories, and thought leadership commentary.

New trends in BI. We will review some of the new trends... Read More

A Road Map to Data Migration Success

  • Source: SAP
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Many significant business initiatives and large IT projects depend upon a successful data migration. But when migrated data is transformed for new uses, project teams encounter some very specific management and technical challenges. Minimizing the risk of these tricky migrations requires effective planning and scoping. Read up on the issues unique to data migration projects, and find out how to best approach them. Read More

Enterprise Information Management: Strategy, Best Practices, and Technologies on Your Path to Success

New data sources, unstructured data, and greater volumes of data than ever before are creating an information overload. Plus, customers’ demands for faster service stress front-office applications, while parallel demands from internal users place greater demands on back-office systems. Before you discover that your data quality has been rendered ineffective, consider adopting enterprise information management (EIM). Read More
 
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