'Fact tables' diaporamas de présentation

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DATAWARE HOUSE INTRODUCTION -QUONTRA SOLUTIONS

We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by Quontra Solutions are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews. Please Visit us for the Demo Classes, we have regular batches and weekend batches. QUONTRASOLUTIONS 204-226 Imperial Drive,Rayners Lane, Harrow-HA2 7HH Phone : 44 (0)20 3734 1498 / 99 Email: info@quontrasolutions.co.uk

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Dimensional Modelling

Dimensional Modelling. By Bob Timlin. Outline . What is a Multi-dimensional Database What is a data-warehouse Review ER-Diagrams Problems with ER for OLAP Purposes. Outline . What is Dimensional Modeling. Star Schemas (Facts and Dimensions) Star Schema vs. ER Diagram SQL Comparison .

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CS 345: Topics in Data Warehousing

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Lecture 3

Lecture 3. Themes in this session Basics of the multidimensional data model and star-join schemata The process of, and specific design issues in, multidimensional data modelling. Why use Multidimensional modelling. Simple, intuitive design basis easy to create a multidimensional model

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Modeling the Data Warehouse

Modeling the Data Warehouse. Chapter 7. Data Warehouse Database Design Phases. Defining the business model (conceptual model) Creating the dimensional model (logical model) Modeling summaries Creating the physical model. Select a business process. 2,3. Physical model.

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SAP BusinessObjects Rapid Marts Prepackaged Analytics Know-How

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CS 345: Topics in Data Warehousing

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Getting Dimensional with Data

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Getting Dimensional with Data

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Presented By Anirban Maiti Chandrashekar Vijayarenu

Dynamic Sample Selection for Approximate Query Processing Brain Babcock (Stanford Univ ) Surajit Chaudhuri (Microsoft Research) Gautam Das (Microsoft Research). Presented By Anirban Maiti Chandrashekar Vijayarenu. Related Work.

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Platinum

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Dimensional model

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Data Warehousing

Data Warehousing. COT4810 4/3/2008. Data Warehousing. Overview Purpose History Methods Design Tools Other aspects of DW. The Purpose of Data Warehousing. Atomic data has a value now Sometimes the value needs to be realized through analytics Reporting Data mining.

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Data Warehouse

Data Warehouse. Week 8 Introduction of Data Warehouse Multidimensional Analysis: OLAP. Data Warehouse. Integrated, Subject-Oriented, Time-Variant, Nonvolatile database that provides support for decision making. Characteristics of Data Warehouse. Integrated Centralized

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Aggregate management

Aggregate management. References: Lawrence Corr Aggregate improvement www.intelligententerprise.com/011004/415warehouse1_1.jhtml Lost, shrunken, and collapsed www.intelligententerprise.com/020101/501warehouse1_1.jhtml Ralph Kimball Aggregate navigation with (almost) no metadata

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Managing your Data Warehouse/ Data Mart Dimensions with MDS

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Data Warehousing

Data Warehousing. DSCI 4103 Dr. Mennecke. Chapter 4. Complex, multi-stage transactions. In some instances, processes might involve multiple conditional transactions (e.g., one type of payment requires a credit check, another requires specialized invoicing, etc.)

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Introduction To Dimensional Modeling

Introduction To Dimensional Modeling. Mike Davis. About Mike Davis. MCTS, MCITP SQL Author Managing Project Lead. Our Agenda. What is Dim Modeling Ten Kimball Rules Dimension Types Examples. What is Dimensional Modeling?. Analyzing OLTP system Business Questions Where clauses

240 views • 10 slides