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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

data dimension  are time sensitive, as data will become available here before it is transformed into the dimension / performance layer. Traditionally this layer is implemented in the Third Normal Form (3NF). Optimizing 3NF Optimizing a 3NF schema in Oracle requires the three Ps – Power, Partitioning and Parallel Execution. Power means that the hardware configuration must be balanced as outlined above. The larger tables or the fact tables should be partitioned using composite partitioning (range-hash or list-hash). Read More...
Outsourcing, IT Infrastructure
The IT Infrastructure Outsourcing knowledge base focuses on the selection of companies who provide outsource services in the areas of information technology (IT) infrastructure. The typical type...
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Documents related to » data dimension


Computer Associates Splashes Into the Data Warehousing Market with Platinum Technology Acquisition
Computer Associates DecisionBase is an Extract/Transform/Load tool designed to help in the population and maintenance of data warehouses. First released in

data dimension  Associates Splashes Into the Data Warehousing Market with Platinum Technology Acquisition Product Background In 1993, a vendor of artificial intelligence software named Trinzic acquired Channel Computing of Portsmouth, NH and inherited a product called InfoPump. InfoPump was a script-based data movement tool for the portion of the data warehousing market known as Extract/Transform/Load (ETL) tools, and was a market leader in the early 1990's. In 1995, Platinum technology International, inc. purchased Read More...
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

data dimension  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? Read More...
TDWI Vegas: How to Build an Enterprise Data Strategy
It takes a lot of commitment, preparation, and time to fully embrace a successful enterprise data strategy. Managing data within a corporation has reached an

data dimension  fact that so much data is being collected and used for so many different purposes is bringing a new dimension to the entire data management space—the management of information has never required more quality care than now. The TDWI conference, being held February 13 to 18 in Vegas, should cover the full spectrum of issues related to establishing a successful data management strategy for your organization. Among the courses being offered, I’m particularly interested in The Art and Practices of Read More...
Master Data Management: Extracting Value from Your Most Important Intangible Asset
In a 2006 SAP survey, 93 percent of respondents experienced data management issues during their most recent projects. The problem: many organizations believe

data dimension  can then analyze consolidated data using a business intelligence solution. Ideally users experience near-real-time search performance, with multiple search mechanisms with every dimension interlinked. You should be able to search an entire repository easily with any item or group of items, and partial strings and equivalents should be indexed to increase positive results. Performance should be measured in milliseconds ' even when managing repositories containing millions of records. BENEFITING FROM Read More...
Overall Approach to Data Quality ROI
Data quality is an elusive subject that can defy measurement and yet be critical enough to derail any single IT project, strategic initiative, or even a company

data dimension  Approach to Data Quality ROI Data quality is an elusive subject that can defy measurement and yet be critical enough to derail any single IT project, strategic initiative, or even a company. Of the many benefits that can accrue from improving the data quality of an organization, companies must choose which to measure and how to get the return on investment (ROI)—in hard dollars. Read this paper to garner an overall approach to data quality ROI. Read More...
A Roadmap to Data Migration Success
Many large business initiatives and information technology (IT) projects depend upon the successful migration of data—from a legacy source, or multiple sources,

data dimension  Roadmap to Data Migration Success Many large business initiatives and information technology (IT) projects depend upon the successful migration of data—from a legacy source, or multiple sources, to a new target database. Effective planning and scoping can help you address the associated challenges and minimize risk for errors. This paper provides insights into what issues are unique to data migration projects and to offer advice on how to best approach them. Read More...
ESG - Riverbed Whitewater: Optimizing Data Protection to the Cloud
Riverbed Whitewater leverages WAN optimization technology to provide a complete data protection service to the cloud. The appliance-based solution is designed

data dimension  - Riverbed Whitewater: Optimizing Data Protection to the Cloud Riverbed Whitewater leverages WAN optimization technology to provide a complete data protection service to the cloud. The appliance-based solution is designed to integrate seamlessly with existing backup technologies and cloud storage provider APIs. Read this ESG Lab report on hands-on testing of the Riverbed Whitewater appliance for ease of use, cost-effective recoverability, data assurance, and performance and scalability. Read More...
Metagenix Reverse Engineers Data Into Information
Metagenix’ MetaRecon reverse engineers metadata information by examining the raw data contained in the source(s) rather than depending on the data dictionaries

data dimension  Reverse Engineers Data Into Information Metagenix Reverse Engineers Data Into Information M. Reed - February 15, 2001 Event Summary Metagenix, Inc. has designed its flagship product, MetaRecon to, as they put it, Decipher Your Data Genome . The product reverse engineers all of the metadata ( data about data ) from data sources and generates information that is very helpful to developers in designing specifications for a new data store, and assists greatly in preparing for cleansing and Read More...
Four Critical Success Factors to Cleansing Data
Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment

data dimension  Success Factors to Cleansing Data Four Critical Success Factors to Cleansing Data If you receive errors when attempting to view this white paper, please install the latest version of Adobe Reader. Data Cleansing and Synchronization Services The pace with which companies are forced to operate and to compete globally has taxed exisitng systems and increased their inefficiencies. Source : PM ATLAS Business Group, LLC Resources Related to Critical Success Factors to Cleansing Data : Data Cleansing Read More...
Implementing Energy-Efficient Data Centers
But in the white paper implementing energy-efficient data centers, you'll learn how to save money by using less electricitywhether your data cente...

data dimension  Energy-Efficient Data Centers Did you realize that your data center(s) may be costing you money by wasting electricity ? Or that there are at least 10 different strategies you can employ to dramatically cut data center energy consumption ? The fact is, most data centers are not designed with energy efficiency in mind. But in the white paper Implementing Energy-efficient Data Centers , you'll learn how to save money by using less electricity—whether your data centers are still in the design Read More...
Oracle Database 11g for Data Warehousing and Business Intelligence
Oracle Database 11g is a database platform for data warehousing and business intelligence (BI) that includes integrated analytics, and embedded integration and

data dimension  Database 11g for Data Warehousing and Business Intelligence Oracle Database 11g is a database platform for data warehousing and business intelligence (BI) that includes integrated analytics, and embedded integration and data-quality. Get an overview of Oracle Database 11g’s capabilities for data warehousing, and learn how Oracle-based BI and data warehouse systems can integrate information, perform fast queries, scale to very large data volumes, and analyze any data. Read More...
Data Mining with MicroStrategy: Using the BI Platform to Distribute Data Mining and Predictive Analytics to the Masses
Data mining and predictive analysis applications can help you make knowledge-driven decisions and improve efficiency. But the user adoption of these tools has

data dimension  BI Platform to Distribute Data Mining and Predictive Analytics to the Masses Data mining and predictive analysis applications can help you make knowledge-driven decisions and improve efficiency. But the user adoption of these tools has been slow due to their lack of business intelligence (BI) functionality, proactive information distribution, robust security, and other necessities. Now there’s an integrated enterprise BI system that can deliver data mining and predictive analysis. Learn more. Read More...
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

data dimension  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. Read More...

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