Data Warehousing with FastTrack and PDW

Oct 19, 2012

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Agenda. SQL Server DataWarehouse Offering OverviewFast Track OfferingMotivationBalanced Architecture Approach for DWExample FastTrack Reference ArchitecturesOptimizing Storage, Load and MaintenanceCase StudiesParallel DataWarouse Offering Overview. Agenda. SQL Server DataWarehouse Offering Ov

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Data Warehousing with FastTrack and PDW

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Data Warehousing and Data Mining

Business Intelligence. The ability of an enterprise to study past behaviors and actions in order to understand where the organization has been, determine its current situation, and predict or change what will happen in the future.. (Imhoff et al. 2003). Evolving Business Intelligence Technologies. .

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Data Warehousing and Data Mining

Data Warehousing and Data Mining. Why Data Mining? — Potential Applications. Database analysis and decision support Market analysis and management target marketing, customer relation management, market basket analysis, cross selling, market segmentation. Risk analysis and management

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Data Warehousing

Virtual University of Pakistan. Data Warehousing . Lecture-1 Introduction and Background. Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research www.nu.edu.pk/cairindex.asp FAST National University of Computers & Emerging Sciences, Islamabad. Introduction and Background.

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DATA WAREHOUSING AND DATA MINING

DATA WAREHOUSING AND DATA MINING. S. Sudarshan Krithi Ramamritham IIT Bombay sudarsha@cse.iitb.ernet.in krithi@cse.iitb.ernet.in. Course Overview. The course: what and how 0. Introduction I. Data Warehousing II. Decision Support and OLAP III. Data Mining IV. Looking Ahead

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Data Warehousing

Data Warehousing. Sushanthan Premanath & Indrajith Premanath CSCI 4707. A Quick Summary. The Main Protagonists. Bill Inmon Born 1945 Father of Data Warehousing. Ralph Kimball Born 1944 Commercialized Data Warehousing. Big Data. The data should be de-normalized to 2NF.

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Data Warehousing

Data Warehousing. Dr. Vairam Arunachalam. Agenda. Value Chain “Clean” construction of DDW Financial Services Subscription Businesses Insurance Factless fact tables Decision Points in DDW construction. Value Chain. Concept: integrated view of value-adding components of business process

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DATA WAREHOUSING

DATA WAREHOUSING. 8201 -DATAWAREHOUSING. …. Metrics suite. 8202 -SYSTEM ANALYSIS AND DESIGN. Cooper, R., and Markus, L.M. Human Reengineering, Rotation, draft, hangen remove 50% of workers, change managers on the spot.

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Data Warehousing

Data Warehousing. Willem Visser RW334. Somebody is watching!. Everybody seems to be recording your every move Loyalty cards Cookies Facebook, Twitter,… Check out Collusion plug-in for Firefox They want to know how to market to you Same is true in business

249 views • 14 slides

Data Warehousing

Virtual University of Pakistan. Data Warehousing. Lecture-7 De-normalization. Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research www.nu.edu.pk/cairindex.asp National University of Computers & Emerging Sciences, Islamabad Email: ahsan@cluxing.com. De-normalization.

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Data Warehousing

Data Warehousing. DSCI 4103 Dr. Mennecke. Chapter 3. Data Warehouse Bus Architecture. The DW bus architecture implies that dimensions in the data warehouse will be conformed Conformed dimensions use common column names and definitions

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Data Warehousing with MySQL

Data Warehousing with MySQL. MySQL. MS-SQL. Oracle. DB2. Flat Files. MySQL. Workshop Conclusion.

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DATA WAREHOUSING AND DATA MINING

DATA WAREHOUSING AND DATA MINING. Mubarak Banisakher. Course Overview. The course: what and how 0. Introduction I. Data Warehousing II. Decision Support and OLAP III. Data Mining IV. Looking Ahead Demos and Labs. 0. Introduction.

671 views • 36 slides

DATA WAREHOUSING

DATA WAREHOUSING. CONTENTS. Introduction Data Mining with Data Warehousing Data Warehouse Architecture Data Warehouse Characteristics OLTP vs. Data Warehouse Data Warehouse to Data Marts For a Successful Warehouse Benefits of Data Warehouse Data Warehouse Pitfalls DW Research Issues

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Data Warehousing and Data Mining

Data Warehousing and Data Mining. Prepared By: Amit Patel http://ajpatelit.hpage.com. Decision Tree. A DECISION TREE is a flowchart-like tree structure, W here each internal node ( nonleaf node) denotes a test on an attribute , E ach branch represents an outcome of the test ,

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Data warehousing with MySQL

Data warehousing with MySQL. MySQL. MS-SQL. Oracle. DB2. Flat Files. MySQL. Free and Open Source Software. MySQL is licensed under GPL.

347 views • 15 slides

Data Warehousing

Data Warehousing. Denis Manley Enterprise Systems FT228/3. Data Warehousing. A data warehouse is a repository for all an organisations data. It is designed to present data in very user-friendly formats to end-users. Why is Data Warehousing necessary?

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Data Warehousing

Virtual University of Pakistan. Data Warehousing. Lecture-22 DQM: Quantifying Data Quality. Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research www.nu.edu.pk/cairindex.asp National University of Computers & Emerging Sciences, Islamabad Email: ahsan101@yahoo.com.

249 views • 15 slides

Data Warehousing

Data Warehousing. Dr. Navneet Goyal Assistant Professor Computer Science Department BITS, Pilani. Background. 1980’s to early 1990’s Focus on computerizing business processes To gain competitive advantage By early 1990’s All companies had operational systems

699 views • 47 slides

DATA WAREHOUSING AND DATA MINING

DATA WAREHOUSING AND DATA MINING. Which are our lowest/highest margin customers ?. Who are my customers and what products are they buying?. What is the most effective distribution channel?. What product prom- -otions have the biggest impact on revenue?.

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Data Warehousing. University of California, Berkeley School of Information Management and Systems SIMS 257: Database Management. Review Application of Object Relational DBMS – the Berkeley Environmental Digital Library Data Warehouses Introduction to Data Warehouses Data Warehousing

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Data Warehousing

Data Warehousing. Enterprise Database Systems. Technological Education Institution of Larisa in collaboration with Staffordshire University Larisa 2007-2008 Dr. Theodoros Mitakos teo_ms@yahoo.com. AGENDA. DATA WAREHOUSES DATA MINING. INTRODUCTION.

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