Enterprise Data Warehouse of ABC University TABLE OF CONTENTS Page CHAPTER 1. INTRODUCTion 3 A. BUSINESS INTELIGENCE 3 B. Data Warehouese 3 C. ETL 3 D. DATA Mart 3 CHAPTER 2. Enterprise 3 A. project plan 3 1. Data Requirements 3 2. Historical Requirements 3 3. Security Requirements 3 4. Performance Requirements 6 B. datAbase design criteria 6 1. The Theory 6 2. Data Modeling 6 3. Unique Identifier 6 4. Database type 6 C. etl process 6 1. Extraction: 6
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ETL_Notes - Pentaho Version ETL, which stands for Extract, Transform and Load, is the process to move data from a source to a destination. I use this generic definition, as the tools are not specific to data warehousing. ETL tools and processes can be used to migrate data in any data context from data warehousing to data migration on an OLTP system update. The rest of this document will focus specifically on ETL issues and issues related to Pentaho Kettle. A good resource for Penaho Kettle is http://wiki
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analysis, graphical representations and access to important data. With the use of dashboards, users at every level can easily access summaries of important information. BI software can combine data from separate warehouses and databases to help managers make better business decisions. With all the information centralized, managers save time when accessing other divisions’ databases. BI uses data mining to automatically sort through large pools of data to determine trends and patterns that could have otherwise
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A process to compile information that increases understanding of how to manage the organization’s relationships with its customers, by it’s simplest definition, is: a. an IT system b. a CRM system c. data mining d. customer retention e. data
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till now have focussed on managing the strategic and tactical business methods and initiatives of organizations. Without Business Intelligence, an organization runs the danger of settling on discriminating choices focused around deficient or wrong data. Settling on choices focused around "gut feel" won't take care of business! One of the fastest BI growth area, lies in its use for managing and optimizing daily business solutions and operations that include overseeing database workloads that answer
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(MIS) Decision-Support Systems (DSS) Data Visualization and Geographic Information Systems Web-Based Customer Decision-Support Systems Group Decision-Support Systems (GDSS) 12.3 EXECUTIVE SUPPORT SYSTEMS (ESS) AND THE BALANCED SCORECARD FRAMEWORK The Role of Executive Support Systems in the Firm Business Value of Executive Support Systems 12.4 HANDS-ON MIS PROJECTS Management Decision Problems Improving Decision Making: Using Pivot Tables to Analyze Sales Data Improving Decision Making: Using a Web-Based
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DBM 380 FULL COURSE http://www.learnyourcourse.com/dbm-380/98-dbm-380-full-course.html DBM 380 WEEK 1 DBM 380 Week 1 DQ # 1- Based on the Manegold (2009) article, describe one method of optimizing data retrieval from a high-use database. (There are many such methods mentioned in the article - pick one you like and do a deep dive.) DBM 380 Week 1 DQ # 2- In the course text, there is some discussion of the capabilities of databases, and recognition of these as an evolution beyond file-based storage
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(<= 50 CHARACTERS) Data Warehousing and Data Mining Bruce Nimo CIS 111 March 19, 2012. Prof Jones Data mining is a process of numerical analysis. Analysts use technical tools to query and sort through terabytes of data looking for patterns. Usually, the analyst will develop a hypothesis, such as customers who buy product X usually buy product Y within six months. Running a query on the relevant data to prove or disprove this theory is data mining. Data warehousing describes the
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the departmental data is already based in Oracle, Microsoft Access and SQL Server and MySQL. The type of data this company specializes in is shopping basket information. These transactions are captured in the millions per day over a 20,000+ network nationwide, and are encrypted to provide security to the shopper identities, which are never linked to their shopping basket data (the use of frequent shopper cards and loyalty programs help this data collection). There is a data warehouse that is comprised
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CSCI 1507 (1903) "Enterprise level data work flows and Data Warehousing" Professor Rajni Palikhey University of Northern Virginia Acknowledgement This Research Paper would not have been possible without the guidance and the help of my co-students and respected Professor who in one way or the other contributed and extended their valuable assistance in the preparation and completion of this research paper. I would to like to convey my sense of gratitude to Professor
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