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Data Warehouse Listings
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Total:
105 | Displaying: 81 - 90 | Pages: << 1 2 3 4 5 6 7 8 9 10 >> |
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An Active Metadata Warehouse is a repository of Metadata to help speed up data reporting and analyses from an active data warehouse. In its most simple definition, a Metadata is data describing data.
Active Data Warehouse is repository of any form of captured transactional data so that they can be used for the purpose of finding trends and patterns to be used for future decision making.
Price: Free - Updated: 12/10/2007
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When companies get ready to implement a data warehouse, few of them pay attention to the political issues that may surround it. It must be emphasized that politics can reduce the chances for success with a data warehouse project, and I want to warn companies against these issues so they can be avoided. The definition of a data warehouse political scenario is when the goals of two parties within the company collide.
Price: Free - Updated: 08/26/2007
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When companies get ready to implement a data warehouse, few of them pay attention to the political issues that may surround it. It must be emphasized that politics can reduce the chances for success with a data warehouse project, and I want to warn companies against these issues so they can be avoided. The definition of a data warehouse political scenario is when the goals of two parties within the company collide.
Price: Free - Updated: 08/26/2007
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This tutorial covers the different types of OLAP models like Relational Online Analytical Processing mode( ROLAP), Multidimensional Online Analytical processing mode(MOLAP) and Hybrid Online Analytical Processing mode or HOLAP.
Updated: 05/29/2006
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This tutorial covers Designing the Dimensional Model, Dimensional Model schemas like Star Schema, Snowflake Schema, Optimizing star schema and Design of the Relational Database, OLAP Cubes and Data mining tools, Security considerations, metadata and backup and recovery plans.
Updated: 05/29/2006
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This tutorial covers the basic design concepts, The top down approach, The Bottom-Up Approach , Hybrid Approach and Federated approach.
Ralph Kimball and Inmon, the co-founders of the data warehouse, significantly had their own differences in the design and architecture of the data warehouse. Inmon advocated a “dependent data mart structure” whereas Kimball advocated the “data warehouse bus structure”.
Updated: 05/29/2006
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This tutorial covers OLAP solutions used by Data warehouses and understanding Data Warehouse design. The enterprise needs to ask itself certain fundamental questions before actually launching on the process of designing the data warehouse. It must begin with a conviction that a data warehouse would really help its business and the return on investment will make it worth it.
Updated: 05/29/2006
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This tutorial starts with the introduction to Data Warehousing, Defination of OLAP, difference between Data warehouse and the OLTP Database, Objectives of data warehousing and data flow.
Updated: 05/29/2006
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In the beginning of data warehouse, a data warehouse was given a simple definition. That definition was (and still is today):
Data warehouse —
+ subject oriented
+ integrated
+ time variant
+ nonvolatile collection of data for management’s decisions.
In addition, we have come to learn that data warehouses are granular. They contain the bedrock data that forms the single source for all DSS processing. With a data warehouse there is reconcilability of information when there are differences of opinion. And the atomic data found in the warehouse can be shaped in many ways, satisfying both known requirements and standing ready to satisfy unknown requirements.
Updated: 05/25/2006
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Data warehouses differ from transactional databases in three main ways.
+ They are bigger terabytes instead of megabytes or gigabytes.
+ They change less often, often daily or at most hourly. If online changes are allowed, they are normally appends.
+ Queries use aggregation or complex WHERE clauses.
The implications of these three points are surprising.
+ Scanning all the data is too slow. Redundant information is needed in the form of special indexes (such as bitmaps or R-trees) or in the form of structures that hold aggregate information. In Chapter 4, we discussed holding aggregate information about total store sales in one table and total sales per vendor in another table. Data warehouses raise such tricks to high art or try to.
+ There is time to build data structures because the data changes slowly or large parts of the data (e.g., all old data) change slowly.
+ Queries that perform aggregates benefit from structures that hold aggregate information. Queries having complex WHERE clauses benefit from query processing engines that can exploit multiple indexes for a single table.
+ Because of the large variety of data warehouse applications (aggregate rich, complex WHERE clause, massive joins), many technologies have survived and have found niches.
Updated: 05/25/2006
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Data Warehouse Listings
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Total:
105 | Displaying: 81 - 90 | Pages: << 1 2 3 4 5 6 7 8 9 10 >> |
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