Business Analytics For All
|
|
- Angelica Carson
- 8 years ago
- Views:
Transcription
1 Business Analytics For All Unlocking the Value within the Data Vault BA4All Insight Session April 29 th 2014 Guy Van der Sande Vincent Greslebin
2 Fifthplay : Architecture Smart Homes Platform Data Warehouse Gebruikers ETL Dag - 1 Data Vault Data mart Marketing & SSC - Controle data kwaliteit - Toepassing business rules - Aggregatie - Filtering Utility Portal Facility Portal
3 Fifthplay : Why Data Vault? Pattern based design which allows agility to take place Easy to add new data sources making it future proof. This allows Fifthplay to stay innovative Large volume of data Build up history that is not available in the operational system Possibility of performing analysis on raw data (cfr quality checks) Development speed (Pilot : 37 working days)
4 Data Vault?
5 Data Vault?
6 Data Vault? The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach encompassing the best of breed between 3rd normal form (3NF) and star schema. The design is flexible, scalable, consistent and adaptable to the needs of the enterprise.
7 Standard architecture The centerpiece of the Enterprise Data Warehouse History is build-up Granularity as detailed as possible No use of business rules Use of business keys that are horizontal in nature and provide visibility across lines of business A new layer which has the benefits of the RAW Data Vault, but with the business data embedded In the Business Data Vault the data has been altered, cleansed and changed to meet the business rules Downstream of the raw data vault Starting point for Master Data Management Metadata is absolutely vital
8 Component parts of the Data Vault model The Data Vault Model exists of 3 basic entity types Hubs : contains a unique list of business keys Links : associations across or between business keys Satellites : holds descriptive data (about the business key) over time
9 Component parts - Hub Represents a Core Business Concept Is formed around the Business Key of this concept Is established the first time a new instance of that business key is introduced Must be 1:1 with a single instance Consists of the business key, a sequence id, a load date/time stamp and a record source.
10 Component parts - Link Represents a natural business relationship between business keys Is established the first time this new unique association is presented Can represent an association between several Hubs and sometimes other Links. maintains a 1:1 relationship with the unique and specific business defined association between that set of keys. Consists of the sequence ids from the Hubs and Links Contains sequence id, a load date/time stamp and a record source.
11 Component parts - Satellite The Satellite contains the descriptive information (context) for a business key. A Satellite can only describe one key (Hub or a Link). The Satellite is the only construct that manages time slice data (data warehouse historical tracking of values over time).
12 Data Vault Why? Dimension 1 Dimension 2 Fact Dimension 3 Dimension 4
13 Data Vault Why? Dimension 1 Dimension 2 Fact Dimension 3 Dimension 4
14 Data Vault Why? Dimension 1 Dimension 2 Fact Fact Dimension 5 Dimension 3 Dimension 4
15 Data Vault Why? DV DM
16 Data Vault Why? S S DV S H L H S S H H DM
17 Data Vault Why? S S DV S H L H S S H H DM Dimension Fact
18 Data Vault How did we do it with Fifthplay?
19 HubServicePartner HubCustomer HubHomeAreaManager HubSmartPlug HubDeviceGroup HubEnergyLogType LinkServicePartnerCustomer LinkCustomerHomeAreaManager LinkHomeAreaManagerSmartPlug LinkCustomerDeviceGroup LinkDeviceGroupSmartPlug LinkDeviceSubGroupSmartPlug LinkSmartPlugApplianceEnergyLogT ype HubCity LinkHomeAreaManagerCity HubCountry LinkCountryCity HubSatServicePartner HubSatCustomer HubSatHomeAreaManager LinkSatHomeAreaManagerCity LinkSatCountryCity HubSatCountry HubSatDeviceGroup HubSatSmartPlug HubAppliance HubSatAppliance LinkSatSmartPlugApplianceEnergyL ogtype HubSatHomeAreaManagerAddress SeqServicePartner ServicePartnerID SeqCustomer CustomerID SeqHomeAreaManager HomeAreaManagerNumber SeqSmartPlug SmartPlugID SeqDeviceGroup DeviceGroupID SeqEnergyLogType EnergyLogName SeqServicePartnerCustomer SeqCustomer SeqServicePartner SeqCustomerHomeAreaMan ager SeqCustomer SeqHomeAreaManager SeqHomeAreaManagerSmar tplug SeqHomeAreaManager SeqSmartPlug SeqCustomerDeviceGroup SeqCustomer SeqDeviceGroup SeqDeviceGroupSmartPlug SeqDeviceGroup SeqDeviceSubGroupSmartPl ug SeqDeviceGroup SeqSmartPlug SeqSmartPlug SeqSmartPlugApplianceEner gylogtype SeqEnergyLogType SeqSmartPlug SeqCity CityPostalCode CityName SeqHomeAreaManagerCity SeqCity SeqHomeAreaManager SeqCountry CountryIsoCode SeqCountryCity SeqCity SeqCountry SeqSatServicePartner SeqServicePartner ServicePartnerCode ServiucePartner ServicePartnerCustomerCon tact SeqSatCustomer SeqCustomer Customer CustomerFirstName CustomerLastName CustomerLanguage SeqSatHomeAreaManager SeqHomeAreaManager HomeAreaManagerMode HomeAreaManagerArchitec ture SeqSatHomeAreaManagerCi ty SeqHomeAreaManagerCity HAMCityAddressLine1 HAMCityPhoneNumber HAMCityAddressLine2 SeqSatCountryCity SeqCountryCity CountryCityRegion CountryCityState SeqSatCountry SeqCountry CountryName SeqSatDeviceGroup SeqDeviceGroup DeviceGroupName DeviceGroupDescription SeqSatSmartPlug SeqSmartPlug SmartPlugDisplayName SmartPlugManufacturer SmartPlugModel SmartPlugIsGenerator SmartPlugHasChildren SmartPlugHasSchedule SeqAppliance ApplianceID SeqSatAppliance SeqAppliance ApplianceCategory SeqSatSmartPlugApplianceE nergylogtype SeqSmartPlugApplianceEner gylogtype EnergyLogDateTime EnergyLogValue SeqAppliance EnergyLogValueUnit Legend Hub Link Satellite ServicePartnerWebPage SeqSatHomeAreaManagerA ddress SeqHomeAreaManager HomeAreaManagerAddress Line1 HomeAreaManagerPostalCo de HomeAreaManagerAddress Line2 HomeAreaManagerCityNam e HomeAreaManagerProvince HomeAreaManagerState HomeAreaManagerCountry Fifthplay Raw Data Vault Architecture
20 Fifthplay Raw Data Vault Architecture HubSmartPlug HubEnergyLogType LinkSmartPlugApplianceEnergyLogT ype HubAppliance HubSatAppliance LinkSatSmartPlugApplianceEnergyL ogtype SeqSmartPlug SmartPlugID SeqEnergyLogType EnergyLogName SeqSmartPlugApplianceEner gylogtype SeqEnergyLogType SeqSmartPlug SeqAppliance ApplianceID SeqSatAppliance SeqAppliance ApplianceCategory SeqSatSmartPlugApplianceE nergylogtype SeqSmartPlugApplianceEner gylogtype EnergyLogDateTime EnergyLogValue SeqAppliance EnergyLogValueUnit Legend Hub Link Satellite
21 Fifthplay : Data Vault lessons learned Don t stop with data vault; A combination with classic dimensional Kimball-methodology is advised Be creative; get out of your comfort zone, dare to walk the thine line While setting up the data vault, operational issues where discovered early in the process ETL-development goes very quickly because of the typical pattern design of the data vault;
22 Data Vault What s next?
23 History and what s next? Relational modeling (E.F.Codd) Bill Inmon began discussing Data Warehousing Barry Devlin and Dr Kimball release Business Data Warehouse Bill Inmon popularizes Data Warehousing Dr Kimball popularizes Star Schema Dan Linstedt begins R&D on Data Vault Modeling Dan Linstedt releases first 5 articles on Data Vault Modeling 2012 : Dan Linstedt announces Data Vault : Dan Linstedt releases Data Vault 2.0 specs
24 Thank You In the Data Warehousing/BI world, we should store the data as it stands on the source system and interpret it on the way out to the data marts. This is absolutely critical to remember. Dan
Trends in Data Warehouse Data Modeling: Data Vault and Anchor Modeling
Trends in Data Warehouse Data Modeling: Data Vault and Anchor Modeling Thanks for Attending! Roland Bouman, Leiden the Netherlands MySQL AB, Sun, Strukton, Pentaho (1 nov) Web- and Business Intelligence
More informationModeling: Operational, Data Warehousing & Data Marts
Course Description Modeling: Operational, Data Warehousing & Data Marts Operational DW DMs GENESEE ACADEMY, LLC 2013 Course Developed by: Hans Hultgren DATA MODELING IMMERSION Modeling: Operational, Data
More informationIntroduction to Data Vault Modeling
Introduction to Data Vault Modeling Compiled and Edited by Kent Graziano, Senior BI/DW Consultant Note: This article is substantially excerpted (with permission) from the book Super Charge Your Data Warehouse:
More informationData Vault and The Truth about the Enterprise Data Warehouse
Data Vault and The Truth about the Enterprise Data Warehouse Roelant Vos 04-05-2012 Brisbane, Australia Introduction More often than not, when discussion about data modeling and information architecture
More informationData Vault at work. Does Data Vault fulfill its promise? GDF SUEZ Energie Nederland
Data Vault at work Does Data Vault fulfill its promise? Leading player on Dutch energy market Approximately 1,000 employees Production capacity: 3,813 MW 20% of the total Dutch electricity production capacity
More informationData Warehouse Overview. Srini Rengarajan
Data Warehouse Overview Srini Rengarajan Please mute Your cell! Agenda Data Warehouse Architecture Approaches to build a Data Warehouse Top Down Approach Bottom Up Approach Best Practices Case Example
More informationTrivadis White Paper. Comparison of Data Modeling Methods for a Core Data Warehouse. Dani Schnider Adriano Martino Maren Eschermann
Trivadis White Paper Comparison of Data Modeling Methods for a Core Data Warehouse Dani Schnider Adriano Martino Maren Eschermann June 2014 Table of Contents 1. Introduction... 3 2. Aspects of Data Warehouse
More informationReflections on Agile DW by a Business Analytics Practitioner. Werner Engelen Principal Business Analytics Architect
Reflections on Agile DW by a Business Analytics Practitioner Werner Engelen Principal Business Analytics Architect Introduction Werner Engelen Active in BI & DW since 1998 + 6 years at element61 Previously:
More informationEast Asia Network Sdn Bhd
Course: Analyzing, Designing, and Implementing a Data Warehouse with Microsoft SQL Server 2014 Elements of this syllabus may be change to cater to the participants background & knowledge. This course describes
More informationIST722 Data Warehousing
IST722 Data Warehousing Components of the Data Warehouse Michael A. Fudge, Jr. Recall: Inmon s CIF The CIF is a reference architecture Understanding the Diagram The CIF is a reference architecture CIF
More informationData Vault Modeling The Next Generation DW Approach
Data Vault Modeling The Next Generation DW Approach Presented by: Siva Govindarajan http://www.globytes.com siva02@globytes.com 1 Data Vault Modeling Agenda Introduction Data Vault Place in Evolution Data
More informationEnterprise Data Warehouse (EDW) UC Berkeley Peter Cava Manager Data Warehouse Services October 5, 2006
Enterprise Data Warehouse (EDW) UC Berkeley Peter Cava Manager Data Warehouse Services October 5, 2006 What is a Data Warehouse? A data warehouse is a subject-oriented, integrated, time-varying, non-volatile
More informationCOURSE OUTLINE. Track 1 Advanced Data Modeling, Analysis and Design
COURSE OUTLINE Track 1 Advanced Data Modeling, Analysis and Design TDWI Advanced Data Modeling Techniques Module One Data Modeling Concepts Data Models in Context Zachman Framework Overview Levels of Data
More informationPart 22. Data Warehousing
Part 22 Data Warehousing The Decision Support System (DSS) Tools to assist decision-making Used at all levels in the organization Sometimes focused on a single area Sometimes focused on a single problem
More informationData Vault Modeling in a Day
Course Description Data Vault Modeling in a Day GENESEE ACADEMY, LLC 2013 Course Developed by: Hans Hultgren DATA VAULT DAY Data Vault Modeling in a Day Overview Data Vault modeling is quickly becoming
More informationData Warehousing. Jens Teubner, TU Dortmund jens.teubner@cs.tu-dortmund.de. Winter 2015/16. Jens Teubner Data Warehousing Winter 2015/16 1
Jens Teubner Data Warehousing Winter 2015/16 1 Data Warehousing Jens Teubner, TU Dortmund jens.teubner@cs.tu-dortmund.de Winter 2015/16 Jens Teubner Data Warehousing Winter 2015/16 13 Part II Overview
More informationAn Introduction to Data Warehousing. An organization manages information in two dominant forms: operational systems of
An Introduction to Data Warehousing An organization manages information in two dominant forms: operational systems of record and data warehouses. Operational systems are designed to support online transaction
More informationOLAP Theory-English version
OLAP Theory-English version On-Line Analytical processing (Business Intelligence) [Ing.J.Skorkovský,CSc.] Department of corporate economy Agenda The Market Why OLAP (On-Line-Analytic-Processing Introduction
More informationSizing Logical Data in a Data Warehouse A Consistent and Auditable Approach
2006 ISMA Conference 1 Sizing Logical Data in a Data Warehouse A Consistent and Auditable Approach Priya Lobo CFPS Satyam Computer Services Ltd. 69, Railway Parallel Road, Kumarapark West, Bangalore 560020,
More informationSENG 520, Experience with a high-level programming language. (304) 579-7726, Jeff.Edgell@comcast.net
Course : Semester : Course Format And Credit hours : Prerequisites : Data Warehousing and Business Intelligence Summer (Odd Years) online 3 hr Credit SENG 520, Experience with a high-level programming
More informationSAS BI Course Content; Introduction to DWH / BI Concepts
SAS BI Course Content; Introduction to DWH / BI Concepts SAS Web Report Studio 4.2 SAS EG 4.2 SAS Information Delivery Portal 4.2 SAS Data Integration Studio 4.2 SAS BI Dashboard 4.2 SAS Management Console
More informationB. 3 essay questions. Samples of potential questions are available in part IV. This list is not exhaustive it is just a sample.
IS482/682 Information for First Test I. What is the structure of the test? A. 20-25 multiple-choice questions. B. 3 essay questions. Samples of potential questions are available in part IV. This list is
More informationLection 3-4 WAREHOUSING
Lection 3-4 DATA WAREHOUSING Learning Objectives Understand d the basic definitions iti and concepts of data warehouses Understand data warehousing architectures Describe the processes used in developing
More informationLooking Back and Surging Ahead
Business Intelligence atunisa Looking Back and Surging Ahead IBM Business Analytics User Group September 2011 Stuart Ainsworth Stuart Ainsworth Planning and Institutional Performance 2011 + Expansion of
More informationCOURSE 20463C: IMPLEMENTING A DATA WAREHOUSE WITH MICROSOFT SQL SERVER
Page 1 of 8 ABOUT THIS COURSE This 5 day course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft SQL Server
More informationImplementing a Data Warehouse with Microsoft SQL Server
Page 1 of 7 Overview This course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft SQL 2014, implement ETL
More informationDimensional Modeling for Data Warehouse
Modeling for Data Warehouse Umashanker Sharma, Anjana Gosain GGS, Indraprastha University, Delhi Abstract Many surveys indicate that a significant percentage of DWs fail to meet business objectives or
More informationCourse 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization
Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing
More informationData Warehousing Fundamentals for IT Professionals. 2nd Edition
Brochure More information from http://www.researchandmarkets.com/reports/2171973/ Data Warehousing Fundamentals for IT Professionals. 2nd Edition Description: Cutting-edge content and guidance from a data
More informationData Warehousing Fundamentals Student Guide
Data Warehousing Fundamentals Student Guide D16310GC10 Edition 1.0 September 2002 D37302 Authors Nikos Psomas Padmaja Mitravinda, Kolachalam Technical Contributors and Reviewers Kasturi Shekhar Vidya Nagaraj
More informationFluency With Information Technology CSE100/IMT100
Fluency With Information Technology CSE100/IMT100 ),7 Larry Snyder & Mel Oyler, Instructors Ariel Kemp, Isaac Kunen, Gerome Miklau & Sean Squires, Teaching Assistants University of Washington, Autumn 1999
More informationChapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization
More informationImplementing a Data Warehouse with Microsoft SQL Server MOC 20463
Implementing a Data Warehouse with Microsoft SQL Server MOC 20463 Course Outline Module 1: Introduction to Data Warehousing This module provides an introduction to the key components of a data warehousing
More informationCOURSE OUTLINE MOC 20463: IMPLEMENTING A DATA WAREHOUSE WITH MICROSOFT SQL SERVER
COURSE OUTLINE MOC 20463: IMPLEMENTING A DATA WAREHOUSE WITH MICROSOFT SQL SERVER MODULE 1: INTRODUCTION TO DATA WAREHOUSING This module provides an introduction to the key components of a data warehousing
More informationImplement a Data Warehouse with Microsoft SQL Server 20463C; 5 days
Lincoln Land Community College Capital City Training Center 130 West Mason Springfield, IL 62702 217-782-7436 www.llcc.edu/cctc Implement a Data Warehouse with Microsoft SQL Server 20463C; 5 days Course
More informationData Virtualization for Agile Business Intelligence Systems and Virtual MDM. To View This Presentation as a Video Click Here
Data Virtualization for Agile Business Intelligence Systems and Virtual MDM To View This Presentation as a Video Click Here Agenda Data Virtualization New Capabilities New Challenges in Data Integration
More informationSeeking Data Quality. Using Agile Methods to Test a Data Warehouse
Seeking Data Quality Using Agile Methods to Test a Data Warehouse Copyright Ideaca 2008 Agenda Seeking Data Quality Data Warehouse Overview The Value of a Data Warehouse Agile as Business Value Driver
More informationBuilding a Data Warehouse
Building a Data Warehouse With Examples in SQL Server EiD Vincent Rainardi BROCHSCHULE LIECHTENSTEIN Bibliothek Apress Contents About the Author. ; xiij Preface xv ^CHAPTER 1 Introduction to Data Warehousing
More informationImplementing a Data Warehouse with Microsoft SQL Server
CÔNG TY CỔ PHẦN TRƯỜNG CNTT TÂN ĐỨC TAN DUC INFORMATION TECHNOLOGY SCHOOL JSC LEARN MORE WITH LESS! Course 20463 Implementing a Data Warehouse with Microsoft SQL Server Length: 5 Days Audience: IT Professionals
More informationNews and trends in Data Warehouse Automation, Big Data and BI. Johan Hendrickx & Dirk Vermeiren
News and trends in Data Warehouse Automation, Big Data and BI Johan Hendrickx & Dirk Vermeiren Extreme Agility from Source to Analysis DWH Appliances & DWH Automation Typical Architecture 3 What Business
More informationData Vault + Data Virtualization = Double Flexibility
Vault + Virtualization = Double Flexibility Copyright 1991-2015 R20/Consultancy B.V., The Hague, The Netherlands. All rights reserved. No part of this material may be reproduced, stored in a retrieval
More informationBusiness Intelligence Solutions. Cognos BI 8. by Adis Terzić
Business Intelligence Solutions Cognos BI 8 by Adis Terzić Fairfax, Virginia August, 2008 Table of Content Table of Content... 2 Introduction... 3 Cognos BI 8 Solutions... 3 Cognos 8 Components... 3 Cognos
More informationData Testing on Business Intelligence & Data Warehouse Projects
Data Testing on Business Intelligence & Data Warehouse Projects Karen N. Johnson 1 Construct of a Data Warehouse A brief look at core components of a warehouse. From the left, these three boxes represent
More informationImplementing a Data Warehouse with Microsoft SQL Server
This course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse 2014, implement ETL with SQL Server Integration Services, and
More informationBIG DATA CAN DRIVE THE BUSINESS AND IT TO EVOLVE AND ADAPT RALPH KIMBALL BUSSUM 2014
BIG DATA CAN DRIVE THE BUSINESS AND IT TO EVOLVE AND ADAPT RALPH KIMBALL BUSSUM 2014 Ralph Kimball Associates 2014 The Data Warehouse Mission Identify all possible enterprise data assets Select those assets
More informationwww.ijreat.org Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 28
Data Warehousing - Essential Element To Support Decision- Making Process In Industries Ashima Bhasin 1, Mr Manoj Kumar 2 1 Computer Science Engineering Department, 2 Associate Professor, CSE Abstract SGT
More informationData Warehousing and Data Mining in Business Applications
133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business
More informationDimensional Modeling and E-R Modeling In. Joseph M. Firestone, Ph.D. White Paper No. Eight. June 22, 1998
1 of 9 5/24/02 3:47 PM Dimensional Modeling and E-R Modeling In The Data Warehouse By Joseph M. Firestone, Ph.D. White Paper No. Eight June 22, 1998 Introduction Dimensional Modeling (DM) is a favorite
More informationKey organizational factors in data warehouse architecture selection
Key organizational factors in data warehouse architecture selection Ravi Kumar Choudhary ABSTRACT Deciding the most suitable architecture is the most crucial activity in the Data warehouse life cycle.
More informationWhat is Management Reporting from a Data Warehouse and What Does It Have to Do with Institutional Research?
What is Management Reporting from a Data Warehouse and What Does It Have to Do with Institutional Research? Emily Thomas Stony Brook University AIRPO Winter Workshop January 2006 Data to Information Historically
More informationA Case Study in Integrated Quality Assurance for Performance Management Systems
A Case Study in Integrated Quality Assurance for Performance Management Systems Liam Peyton, Bo Zhan, Bernard Stepien School of Information Technology and Engineering, University of Ottawa, 800 King Edward
More informationData Management, Analytics and Business Intelligence
TECHNOLOGY TRANSFER PRESENTS Rome, June 25-26 2015 Residenza di Ripetta Via di Ripetta, 231 INTERNATIONAL SUMMIT 2 0 1 5 Data Management, Analytics and Business Intelligence A B O U T T H E S U M M I T
More informationImplementing a Data Warehouse with Microsoft SQL Server 2012
Implementing a Data Warehouse with Microsoft SQL Server 2012 Module 1: Introduction to Data Warehousing Describe data warehouse concepts and architecture considerations Considerations for a Data Warehouse
More informationMaster Data Management and Data Warehousing. Zahra Mansoori
Master Data Management and Data Warehousing Zahra Mansoori 1 1. Preference 2 IT landscape growth IT landscapes have grown into complex arrays of different systems, applications, and technologies over the
More informationA Service-oriented Architecture for Business Intelligence
A Service-oriented Architecture for Business Intelligence Liya Wu 1, Gilad Barash 1, Claudio Bartolini 2 1 HP Software 2 HP Laboratories {name.surname@hp.com} Abstract Business intelligence is a business
More informationData Vault and Data Virtualization: Double Agility
Data Vault and Data Virtualization: Double Agility A Technical Whitepaper Rick F. van der Lans Independent Business Intelligence Analyst R20/Consultancy March 2015 Sponsored by Copyright 2015 R20/Consultancy.
More informationA Design and implementation of a data warehouse for research administration universities
A Design and implementation of a data warehouse for research administration universities André Flory 1, Pierre Soupirot 2, and Anne Tchounikine 3 1 CRI : Centre de Ressources Informatiques INSA de Lyon
More informationMethodology Framework for Analysis and Design of Business Intelligence Systems
Applied Mathematical Sciences, Vol. 7, 2013, no. 31, 1523-1528 HIKARI Ltd, www.m-hikari.com Methodology Framework for Analysis and Design of Business Intelligence Systems Martin Závodný Department of Information
More informationThe New Face of Business Intelligence for SAP Customers
Business Objects, an SAP company The New Face of Business Intelligence for SAP Customers Place holder Dan Kearnan, SAP BI Marketing, Business Objects Ken Hartman, Hughes Network Systems Agenda Why SAP
More informationwww.ducenit.com Analance Data Integration Technical Whitepaper
Analance Data Integration Technical Whitepaper Executive Summary Business Intelligence is a thriving discipline in the marvelous era of computing in which we live. It s the process of analyzing and exploring
More informationBringing agility to Business Intelligence Metadata as key to Agile Data Warehousing. 1 P a g e. www.analytixds.com
Bringing agility to Business Intelligence Metadata as key to Agile Data Warehousing 1 P a g e Table of Contents What is the key to agility in Data Warehousing?... 3 The need to address requirements completely....
More informationData Warehousing Systems: Foundations and Architectures
Data Warehousing Systems: Foundations and Architectures Il-Yeol Song Drexel University, http://www.ischool.drexel.edu/faculty/song/ SYNONYMS None DEFINITION A data warehouse (DW) is an integrated repository
More informationEstablish and maintain Center of Excellence (CoE) around Data Architecture
Senior BI Data Architect - Bensenville, IL The Company s Information Management Team is comprised of highly technical resources with diverse backgrounds in data warehouse development & support, business
More informationName: Clint Huijbers Function: Senior Microsoft Business Intelligence consultant
Name: Function: Senior Microsoft Business Intelligence consultant Residence: Eindhoven, The Netherlands Birth date: 03-07-1985 Available per: On request Availability (in hours): 40 hours per week Motivation
More informationLife Cycle of a Data Warehousing Project in Healthcare
Life Cycle of a Data Warehousing Project in Healthcare Ravi Verma, Jeannette Harper ABSTRACT Hill Physicians Medical Group (and its medical management firm, PriMed Management) early on recognized the need
More informationCHAPTER SIX DATA. Business Intelligence. 2011 The McGraw-Hill Companies, All Rights Reserved
CHAPTER SIX DATA Business Intelligence 2011 The McGraw-Hill Companies, All Rights Reserved 2 CHAPTER OVERVIEW SECTION 6.1 Data, Information, Databases The Business Benefits of High-Quality Information
More informationCourse Outline: Course: Implementing a Data Warehouse with Microsoft SQL Server 2012 Learning Method: Instructor-led Classroom Learning
Course Outline: Course: Implementing a Data with Microsoft SQL Server 2012 Learning Method: Instructor-led Classroom Learning Duration: 5.00 Day(s)/ 40 hrs Overview: This 5-day instructor-led course describes
More informationData Warehousing: A Technology Review and Update Vernon Hoffner, Ph.D., CCP EntreSoft Resouces, Inc.
Warehousing: A Technology Review and Update Vernon Hoffner, Ph.D., CCP EntreSoft Resouces, Inc. Introduction Abstract warehousing has been around for over a decade. Therefore, when you read the articles
More informationChapter 5. Warehousing, Data Acquisition, Data. Visualization
Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization 5-1 Learning Objectives
More informationVIEWPOINT. High Performance Analytics. Industry Context and Trends
VIEWPOINT High Performance Analytics Industry Context and Trends In the digital age of social media and connected devices, enterprises have a plethora of data that they can mine, to discover hidden correlations
More informationImplementing a Data Warehouse with Microsoft SQL Server
Course Code: M20463 Vendor: Microsoft Course Overview Duration: 5 RRP: 2,025 Implementing a Data Warehouse with Microsoft SQL Server Overview This course describes how to implement a data warehouse platform
More informationwww.sryas.com Analance Data Integration Technical Whitepaper
Analance Data Integration Technical Whitepaper Executive Summary Business Intelligence is a thriving discipline in the marvelous era of computing in which we live. It s the process of analyzing and exploring
More informationWhitepaper. Data Warehouse/BI Testing Offering YOUR SUCCESS IS OUR FOCUS. Published on: January 2009 Author: BIBA PRACTICE
YOUR SUCCESS IS OUR FOCUS Whitepaper Published on: January 2009 Author: BIBA PRACTICE 2009 Hexaware Technologies. All rights reserved. Table of Contents 1. 2. Data Warehouse - Typical pain points 3. Hexaware
More informationChapter 5. Learning Objectives. DW Development and ETL
Chapter 5 DW Development and ETL Learning Objectives Explain data integration and the extraction, transformation, and load (ETL) processes Basic DW development methodologies Describe real-time (active)
More information2074 : Designing and Implementing OLAP Solutions Using Microsoft SQL Server 2000
2074 : Designing and Implementing OLAP Solutions Using Microsoft SQL Server 2000 Introduction This course provides students with the knowledge and skills necessary to design, implement, and deploy OLAP
More informationPresented by: Jose Chinchilla, MCITP
Presented by: Jose Chinchilla, MCITP Jose Chinchilla MCITP: Database Administrator, SQL Server 2008 MCITP: Business Intelligence SQL Server 2008 Customers & Partners Current Positions: President, Agile
More informationExtensibility of Oracle BI Applications
Extensibility of Oracle BI Applications The Value of Oracle s BI Analytic Applications with Non-ERP Sources A White Paper by Guident Written - April 2009 Revised - February 2010 Guident Technologies, Inc.
More informationCourse 20463:Implementing a Data Warehouse with Microsoft SQL Server
Course 20463:Implementing a Data Warehouse with Microsoft SQL Server Type:Course Audience(s):IT Professionals Technology:Microsoft SQL Server Level:300 This Revision:C Delivery method: Instructor-led (classroom)
More informationWhen to consider OLAP?
When to consider OLAP? Author: Prakash Kewalramani Organization: Evaltech, Inc. Evaltech Research Group, Data Warehousing Practice. Date: 03/10/08 Email: erg@evaltech.com Abstract: Do you need an OLAP
More informationUnderstanding Data Warehousing. [by Alex Kriegel]
Understanding Data Warehousing 2008 [by Alex Kriegel] Things to Discuss Who Needs a Data Warehouse? OLTP vs. Data Warehouse Business Intelligence Industrial Landscape Which Data Warehouse: Bill Inmon vs.
More informationData Warehousing and Data Mining
Data Warehousing and Data Mining Part I: Data Warehousing Gao Cong gaocong@cs.aau.dk Slides adapted from Man Lung Yiu and Torben Bach Pedersen Course Structure Business intelligence: Extract knowledge
More informationETL-EXTRACT, TRANSFORM & LOAD TESTING
ETL-EXTRACT, TRANSFORM & LOAD TESTING Rajesh Popli Manager (Quality), Nagarro Software Pvt. Ltd., Gurgaon, INDIA rajesh.popli@nagarro.com ABSTRACT Data is most important part in any organization. Data
More informationTesting Data Vault-Based Data Warehouse
Georgia Southern University Digital Commons@Georgia Southern Electronic Theses & Dissertations Jack N. Averitt College of Graduate Studies (COGS) Fall 2015 Testing Data Vault-Based Data Warehouse Connard
More informationOBIEE - The Rising Sun
Together we re a bestseller OBIEE - The Rising Sun Leaving stars and snow behind Emiel van Bockel Centraal Boekhuis Introduction Emiel van Bockel - Manager Information Services - Bachelor Information Engineering
More informationRepublic Polytechnic School of Information and Communications Technology C355 Business Intelligence. Module Curriculum
Republic Polytechnic School of Information and Communications Technology C355 Business Intelligence Module Curriculum This document addresses the content related abilities, with reference to the module.
More informationObjectives RAW Data Vault Staging RAW Data Vault Completeness all all History no yes Structure simple tables + metadata Data Vault + metadata Validations yes minimal Transformations no no Optimize - optimized
More informationThe COSMIC Functional Size Measurement Method Version 3.0
The COSMIC Functional Size Measurement Method Version 3.0 Guideline for sizing Data Warehouse Application Software Version 1.0, May 2009 Acknowledgements This COSMIC Guideline is derived from a paper originally
More informationKnowledgent White Paper Series. Developing an MDM Strategy WHITE PAPER. Key Components for Success
Developing an MDM Strategy Key Components for Success WHITE PAPER Table of Contents Introduction... 2 Process Considerations... 3 Architecture Considerations... 5 Conclusion... 9 About Knowledgent... 10
More information8. Business Intelligence Reference Architectures and Patterns
8. Business Intelligence Reference Architectures and Patterns Winter Semester 2008 / 2009 Prof. Dr. Bernhard Humm Darmstadt University of Applied Sciences Department of Computer Science 1 Prof. Dr. Bernhard
More informationPOLAR IT SERVICES. Business Intelligence Project Methodology
POLAR IT SERVICES Business Intelligence Project Methodology Table of Contents 1. Overview... 2 2. Visualize... 3 3. Planning and Architecture... 4 3.1 Define Requirements... 4 3.1.1 Define Attributes...
More informationMaster Data Management. Zahra Mansoori
Master Data Management Zahra Mansoori 1 1. Preference 2 A critical question arises How do you get from a thousand points of data entry to a single view of the business? We are going to answer this question
More informationEvaluating Data Warehousing Methodologies: Objectives and Criteria
Evaluating Data Warehousing Methodologies: Objectives and Criteria by Dr. James Thomann and David L. Wells With each new technical discipline, Information Technology (IT) practitioners seek guidance for
More informationBasics of Dimensional Modeling
Basics of Dimensional Modeling Data warehouse and OLAP tools are based on a dimensional data model. A dimensional model is based on dimensions, facts, cubes, and schemas such as star and snowflake. Dimensional
More informationDocumenting ETL Rules using CA Erwin Data Modeler. By Sampath Kumar
Documenting ETL Rules using CA Erwin Data Modeler By Sampath Kumar Abstract In any data warehouse development project some of the major challenges include Effective capture and maintenance of metadata
More informationNext Generation Business Performance Management Solution
Next Generation Business Performance Management Solution Why Existing Business Intelligence (BI) Products are Inadequate Changing Business Environment In the face of increased competition, complex customer
More informationAdvanced Data Management Technologies
ADMT 2015/16 Unit 2 J. Gamper 1/44 Advanced Data Management Technologies Unit 2 Basic Concepts of BI and Data Warehousing J. Gamper Free University of Bozen-Bolzano Faculty of Computer Science IDSE Acknowledgements:
More informationDesigning Agile Data Pipelines. Ashish Singh Software Engineer, Cloudera
Designing Agile Data Pipelines Ashish Singh Software Engineer, Cloudera About Me Software Engineer @ Cloudera Contributed to Kafka, Hive, Parquet and Sentry Used to work in HPC @singhasdev 204 Cloudera,
More informationMICHAEL SCHMITZ NOVEMBER 20-22, 2006 NOVEMBER 23-24, 2006 RESIDENZA DI RIPETTA - VIA DI RIPETTA, 231 ROME (ITALY)
TECHNOLOGY TRANSFER PRESENTS MICHAEL SCHMITZ DATA WAREHOUSING Advanced Design and Implementation Issues ETL FOR THE DATA WAREHOUSE A Template-Driven Approach NOVEMBER 20-22, 2006 NOVEMBER 23-24, 2006 RESIDENZA
More informationQAD Business Intelligence Data Warehouse Demonstration Guide. May 2015 BI 3.11
QAD Business Intelligence Data Warehouse Demonstration Guide May 2015 BI 3.11 Overview This demonstration focuses on the foundation of QAD Business Intelligence the Data Warehouse and shows how this functionality
More information