...Data Warehousing Saikrishna Burugula IST 7000 Data Management Wilmington University Abstract A data storage could be a subject-oriented, integrated, time-variant, non-updateable assortment of knowledge utilized in business intelligence and support of organizing decision-making method (Inmon, Strauss & Neushloss, 2008). In data warehousing when the data is stored it is not updated, commonly data warehousing intended for evaluation connected with data source in addition to addressing queries it can be called copy of addressing data (Prabhu, 2002). The key intention with this paper is typically to target on the actual design connected with data warehouse in addition to modeling techniques like ER modeling and Dimensional modeling. Introduction A Data Warehouse is not just a new combination of all of the in business databases in an organization. Because of its attention on business intelligence, exterior data, and time variant information, a data warehouse is usually a special type of database. The good thing is, you should not learn another number of database abilities to do business with a new information storage place. Most data warehouses tend to be relational databases designed in many ways optimized pertaining to selection assistance, definitely not in business information running. Facts warehousing could be the procedure whereby organizations create and gaze after information...
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...Running head: SHORT TITLE OF PAPER (<= 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 process of designing how the data is stored in order to improve reporting and analysis. Data warehouse experts consider that the various stores of data are connected and related to each other conceptually as well as physically. A business's data is usually stored across a number of databases. However, to be able to analyze the broadest range of data, each of these databases needs to be connected in some way. This means that the data within them need a way of being related to other relevant data and that the physical databases themselves have a connection so their data can be looked at together for reporting purposes. Businesses then use this information to make better business decisions based on how they understand their customers' and suppliers' behaviors. Examples of businesses that use data warehousing and data mining are amason.com, Wal-Mart stores Inc etc. Both data mining and data warehousing are business intelligence tools that are used to turn...
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...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 2. Transfer: 6 3. Load: 6 D. data mart 6 E. data Mining 6 CHAPTER 3. summary 6 A. datawarehosue 6 B. ETL 6 C. data mart 6 D. data mining 6 CHAPTER 4. work cited 6 INTRODUCTion BUSINESS INTELIGENCE Business Intelligence is not a new tactic of reviewing data as there had been traditional tools to analyze data to be able to deliver value to the owners of those data (Souhard Joens, Birst, 8/21/14). How to benefit from the vast data which we had gathered, planned or by default, how this data can be consolidated, and is there any way to make sense of these data have been the drive behind the new tools presented to be able to arrive at some conformity and then being able to use the analytically to better the entity, whatever the mission. Data Warehouese Data Warehouse is a database which holds vast amount of data different in the nature and type in various tables inside in a manner which they can be accessed and manipulated to generate intelligent reports for analytical...
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...Paymentech, was integrated with Yahoo! to validate credit cards and process payments for Cliptomania. Marketing was another issue encountered. With the dot-com bust in the early 2000s, the cost of listing the website in search engines increased based on the number of “clicks.” The Santos soon found, however, that this increased cost resulted in increased sales and now consider it an acquisition cost. Cliptomania also ventured into search engine advertising and pay for searches based on keywords that are selected by the company. ------------------------------------------------- SOLUTIONS (Taken by the company) (20 Points)* * Invest in an enterprise data warehouse and moving real-time data to warehouse. * Fare Design: Continental uses real-time data to optimize airfares. * Ticket Facsimile * The warehouse team developed an application for...
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...Data Warehousing Methodologies Using a common set of attributes to determine which methodology to use in a particular data warehousing project. DATA INTEGRATION TECHNOLOGIES have experienced explosive growth in the last few years, and data warehousing has played a major role in the integration process. A data warehouse is a subjectoriented, integrated, time-variant, and nonvolatile collection of data that supports managerial decision making [4]. Data warehousing has been cited as the highest-priority post-millennium project of more than half of IT executives. A large number of data warehousing methodologies and tools are available to support the growing market. However, with so many methodologies to choose from, a major concern for many firms is which one to employ in a given data warehousing project. In this article, we review and compare several prominent data warehousing methodologies based on a common set of attributes. Online transaction processing (OLTP) systems are useful for addressing the operational data needs of a firm. However, they are not well suited for supporting decision-support queries or business questions that managers typically need to address. Such questions involve analytics including aggregation, drilldown, and slicing/dicing of data, which are best supported by online analytical processing (OLAP) systems. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Data in an OLAP warehouse is extracted and loaded...
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...Objective To become Data warehousing Techno-Functional Consultant Current Role: Team member Synopsis of Experience: ➢ Over 2+ years of IT experience in Application Development, Testing of Data Warehousing Projects. ➢ Worked extensively on ETL tool (Informatica 9.1). ➢ Working knowledge on BI Tool (BOXI) ➢ Good exposure to data analysis, data extraction and data loading. ➢ Extensively worked on Oracle 10G. ➢ Strong understanding of Dimensional Modeling, Star and Snowflake Schemas. ➢ Having good inter-personal, analytical and communication skills. Work Experience: ➢ Have been working since Dec 2011 to Till Date as Developer in Vertiv solutions (INDIA) pvt ltd. Education: B.Tech from Jawaharlal Nehru Technological University Technical Skills: Operating System : MS-DOS, Window 7, UNIX Database : Oracle 9i, MS-SQL Server ETL Tool : Informatica Power Center 9.1 OLAP Tool : Business Objects Scheduler : Autosys Other Utilities : TOAD 8.0 Project Information: Project#1 Vertiv solutions (INDIA) pvt ltd. Client: Hewlett Packard, Houston USA Project: E-WARRANTY MANAGEMENT SYSTEM Duration: Dec 2011 – Mar 2013 Environment: Windows XP, UNIX, Informatica 7.1.2, Business Objects 6.5, Oracle9i, TOAD8.0, MS-SQL Server2000, MS-DTS About the Client: Hewlett Packard...
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...Business Intelligence and Data Warehouses Kevin Gainey Mr. Brown CIS 111 Data warehouses support business decisions by collecting, consolidating, and organizing data for reporting and analysis with tools such as online analytical processing (OLAP) and data mining. Although data warehouses are built on relational database technology, the design of a data warehouse database differs substantially from the design of an online transaction processing system (OLTP) database. The topics in this paper address approaches and choices to be considered when designing and implementing a data warehouse. The paper begins by contrasting data warehouse databases with OLTP databases and introducing OLAP and data mining, and then adds information about design issues to be considered when developing a data warehouse with Microsoft® SQL Server™ 2000. A data warehouse supports an OLTP system by providing a place for the OLTP database to offload data as it accumulates, and by providing services that would complicate and degrade OLTP operations if they were performed in the OLTP database. Without a data warehouse to hold historical information, data is archived to static media such as magnetic tape, or allowed to accumulate in the OLTP database. If data is simply archived for preservation, it is not available or organized for use by analysts and decision makers. If data is allowed to accumulate in the OLTP so it can be used for analysis, the OLTP database continues to grow in size and...
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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.Rajni Palikhey who helped and supported us right throughout the semester. This paper would not have been possible without her cooperation and technical assistance. We would also thank our Institution and our faculty members without whom this project would have been a distant reality. We also extend our heartfelt thanks to our family and well wishers. I would like to take this occasion to specially thank University of Northern Virginia to provide us with excellent faculty and also in supporting us getting quality education remotely. Contents SL No Title Page no 1 Abstract 5 2 Introduction to Databases 6 3 OLTP and OLAP Systems 7 4 Difference between OLTP and OLAP 9 5 Data Modeling 13 6 Workflows in Enterprise level Data warehousing 18 7 Business Intelligence tools used in Data flow and Data Warehousing 21 8 Analysis in Data warehousing 24 9 Conclusion 28 10 Foot Note 30 11 References 31 ABSTRACT These days majority...
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...FIRST QUARTER 2007 PREDICTIVE ANALYTICS Extending the Value of Your Data Warehousing Investment By Wayne W. Eckerson Research Sponsors MicroStrategy, Inc. OutlookSoft Corporation SAS SPSS Sybase, Inc. Teradata, a division of NCR www.tdwi.org PREDICTIVE ANALYTICS Extending the Value of Your Data Warehousing Investment By Wayne W. Eckerson Table of Contents Research Methodology and Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 What Is Predictive Analytics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 The Business Value of Predictive Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Measuring Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 How Do You Deliver Predictive Analytics? . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 11 The Process of Predictive Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1. Defining the Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2. Exploring the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3. Preparing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....
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...What is Data Warehousing? A data warehouse can be defined as follows: • subject oriented • integrated • time-variant • nonvolatile It is a collection of data in support of management decision-making process. Benefits of Data Warehousing Data warehousing is intended to support reporting and analysis of data. Here are the benefits as follows: • Potential High Returns on Investment • Competitive Advantage • Increased Productivity of Corporate Decision Makers Problems of Data Warehousing Here are some problems associated with developing and maintaining a data warehouse as follows: • Underestimation of Resources for Data Loading • Hidden Problems with Source Systems • Required Data not Captured • Required Data not Captured • Increased End User Demands • Data Homogenization • High Demand for Resources • Data Ownership • High Maintenance • Long Duration Projects • Complexity of Integration Data Warehouse Architecture Operational Data Store • A repository of current and integrated operational data used for analysis Load Manager • Performs all the operations associated with the extraction and loading of data into the extraction and loading of data into the warehouse Warehouse Manager • Performs all the operations associated with the management of data in the warehouse Query Manager • Performs all the operations associated...
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...Data Warehouses The basic reasons organizations implement data warehouses are: To perform server/disk bound tasks associated with querying and reporting on servers/disks not used by transaction processing systems most firms want to set up transaction processing systems so there is a high probability that transactions will be completed in what is judged to be an acceptable amount of time. Reports and queries, which can require a much greater range of limited server/disk resources than transaction processing, run on the servers/disks used by transaction processing systems can lower the probability that transactions complete in an acceptable amount of time. Or, running queries and reports, with their variable resource requirements, on the servers/disks used by transaction processing systems can make it quite complex to manage servers/disks so there is a high enough probability that acceptable response time can be achieved. Firms therefore may find that the least expensive and/or most organizationally expeditious way to obtain high probability of acceptable transaction processing response time is to implement a data warehousing architecture that uses separate servers/disks for some querying and reporting. To use data models and/or server technologies that speed up querying and reporting and that are not appropriate for transaction processing There are ways of modeling data that usually speed up querying and reporting (e.g., a star schema) and may not be appropriate for transaction...
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...Top Data Management Terms to Know Fifteen essential definitions you need to know Fifteen Essential Data Management Terms We know it’s not always easy to keep up-to-date Contents with the latest data management terms. That’s why we have put together the top fifteen terms and definitions that you and your peers need to know. OLAP (online analytical processing) Star schema What is OLAP (online analytical processing) Fact table OLAP (online analytical processing) is computer processing that enables a Big data analytics Data modeling Ad hoc analysis user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Data visualization Extract, transform, load (ETL) Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time Association rules (in data mining) Relational database period) as a separate "dimension." OLAP software can locate the intersection of dimensions (all products sold in the...
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...Health – A Cerner data warehouse in 90 days - Case Study http://www.healthcatalyst.com/success_stories/how-to-deliver-healthcare-EDW-in-90-days/?utm_medium=cpc&utm_campaign=Data+Warehouse&utm_source=bing&utm_term=+data%20+warehousing%20+case%20+study&utm_content=3542719787 Name: Goutham Para Provide brief but complete answers. One page maximum (print preview to make sure it does not exceed one-two pages). Q1: Describe the original data warehouse designed for Indiana University Health and its limitations. Please describe the new data warehouse and the differences between each? The original data warehouse structured and designed for Indiana University Health is traditional enterprise data warehouse. They designed data warehouse by using early binding architecture. There would be errors it takes months to update (health catalyst). Indiana University developed a new data warehouse health catalyst with help of late binding architecture. They promised to complete the work within 90 days as soon as possible with no risk. Health catalyst gave deadline data of 14 billion rows in to Enterprise Data warehouse (EDW), it is totally clinical data for ten years of Indiana university’s health network (health catalyst). The observed difference between both data warehouses is old and slow process. Considering health catalyst is faster for storing enormous data very fast without any faults. Q2: Identify the major differences between a traditional data warehouse and a data mart? Explain the...
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...explore how a company can become an analytic competitor. Questions as to what are the sources of Decision Making to an analytic competitor will also be discussed. A discussion on how influential quantitative modeling is and its utility in business decision making will be summarized. Finally, a Christian view that presents an ethical perspective on quantitative modeling and decision making will be presented. In an ever increasing global environment, maintaining a competitive advantage can be sustained through quantitative modeling, which can make a company a viable analytic competitor. How Can a Company Become and Be an Analytics Competitor Competitors make it increasingly more difficult to maintain a strategic competitive advantage when exclusive technologies, products and services can be duplicated (Davenport, Cohen & Jacobson, 2005). Organizations are now framing their strategies to accomplish optimization of “key business processes”: serving optimal customers, optimize supply chains, and understand and create optimal financial performance (Davenport, et al., 2005, p. 1). Optimization strategies demand that organizations now gather extensive data and perform extensive analysis that will guide executives in the decision-making process. The data and the analysis must be specific to the issues. In other words, they must relate in order to be useful in decision-making for the firm. In order to benefit from this competitive advantage companies also have to possess...
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...Training Curriculum Data Warehousing: • Introduction to Business Objects Enterprise Reporting • Fundamentals of Data warehouse Concepts • Introduction to Dimensional Modeling • Developing a Star Schema Reporting: • Building and editing queries with Web Intelligence • Performing on report analysis with Web Intelligence • Filtering Queries using conditions, prompts etc., • Using Combined Queries and merging dimensions • Displaying data in various formats (Ex: Tables, Charts etc.,) Advanced Reporting: • Calculations, Formulas and variables • Ranking Data, using Alerters to highlight data, Formatting numbers and Dates • Understanding Calculation Contexts • Web Intelligence Functions, Operators and Keywords • Calculating values with Smart Measures Universe Designer: • Designer and Universe Fundamentals • Creating a schema with Tables and Joins • Resolving Join problems in a schema • Defining Classes, Objects, hierarchies, using cascading list of values for hierarchies • Testing the universe • Working with OLAP universes Xcelsius 2008: • Application Overview • Creating and Updating Xcelsius visualizations • Using Xcelsius components ( Chart, Containers, Selectors etc.,) • Exporting Xcelsius visualizations to various applications (Power point, PDF, Flash • Creating templates, Alerts and Dynamic visibility • Using Data Manager ( Creating and configuring connections) • Live Office Connections, Query As A Web Service (QWAAS), XML data Connections Crystal...
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