We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byAlonzo Searle
Modified about 1 year ago
Copyright © Starsoft Inc, Data Warehouse Architecture By Slavko Stemberger
Copyright © Starsoft Inc, Some Acronyms/Terms OLAP –On-line Analytical Processing ROLAP –Relational OLAP OLTP –On-Line Transaction Processing (operational system)
Copyright © Starsoft Inc, Some Acronyms/Terms Metadata –Data about data (data dictionary) Source System –An operational system that provides data for the data warehouse MOLAP –Multidimensional OLAP
Copyright © Starsoft Inc, Some Acronyms/Terms Data Warehouse –A queryable source of data Data Mart –A logical subset of a data warehouse Data Staging Area –An intermediate storage location used for ETL ETL –Extract, Transform and Load
Copyright © Starsoft Inc, Data Structures/Databases Hierarchical DB Network DB Relational DB O-O DB Dimensional DB Flat Files
Copyright © Starsoft Inc, Modeling Methods Dimensional Object Oriented (O-O) Entity-Relationship (E-R)
Copyright © Starsoft Inc, Entity-Relationship Modeling Instantaneous snapshot of the business Removed data redundancy (eliminates update anomalies) Shows detail relationships Complex network of entities can be difficult for end-users to understand Used for operational system
Copyright © Starsoft Inc, Dimensional Modeling Data duplication is allowed (in the dimensions) Query based Easier for users to understand –Not as much detail shows as in E-R Used in data warehouses
Copyright © Starsoft Inc, Dimensional Models Star Schema Snowflake Schema The “Cube”
Copyright © Starsoft Inc, The “Cube” Logical structure of ALL data warehouses Can be implemented physically in an RDB like Oracle Some view this as limited to data marts
Copyright © Starsoft Inc, Star Schema Easy to understand Flexible in type of questions that can be asked Supports very large data warehouses There is data redundancy (in the dimensions)
Copyright © Starsoft Inc, Snowflake Schema “Normalized” star schema More complex than the star schema - harder to understand and work with Solves some problems that cannot be done with star schema
Copyright © Starsoft Inc, Dimension Tables Each variable has a set of known, relatively small, set of values dimensions per data warehouse/data mart is the norm A set of independent variables that affect an observation
Copyright © Starsoft Inc, Dimension Tables (cont…) Some numeric values are descriptive –Numeric descriptive values should be suspect of being facts e.g. standard product price may be a fact because it can change and one can ask “what was the average standard price of the product over the last 12 months” Columns are descriptive and usually textual
Copyright © Starsoft Inc, Dimension Tables (cont…) Time dimension keys may be/should be assigned in the order of the dates in the fact table - this allows physical partitioning In general avoid “smart” keys - they should be meaningless Avoid production keys Dimension keys should be meaningless surrogate keys
Copyright © Starsoft Inc, Dimension Tables - Granularity Keep the grain of the data as small as possible (as detail as possible) –This makes the warehouse more resistant to change –It is easier to add attributes to existing dimensions –superior results in data mining operations Definition: The level of detail of the data
Copyright © Starsoft Inc, Dimension Tables - “Types” Degenerate “Junk” Other Time
Copyright © Starsoft Inc, Dimension Tables - Time Must be consistent across all fact tables Create partial attributes year, month and day and their concatenations (year + month, year + month + day, year + week, …) –Without the concatenations, it is difficult to ask for time ranges All data marts and warehouses have at least one time dimension
Copyright © Starsoft Inc, Dimension Tables - Degenerate Usually a control document id such as order number, invoice number, etc No value in creating a physical table Put the id into the fact table Dimensions with only one attribute
Copyright © Starsoft Inc, Dimension Tables - “Junk” Possible Actions: –Put the these flags into the fact table –Make each one into a dimension –Drop them from the design –Create one dimension with all combinations of these flags Given: Leftover flags and text attributes
Copyright © Starsoft Inc, Fact Tables Degenerate dimension keys (if they exist) Facts –Additive –Semi-additive –Non-additive –None (factless tables) Dimension keys
Copyright © Starsoft Inc, Facts - Additive Can be added across all combination of dimensions Examples: sales in dollars or units These are measures of activity
Copyright © Starsoft Inc, Facts - Semi-additive/non- additive Some may be added across some dimensions but not others –e.g. Bank Balance Some may not be added at all –e.g. Temperature These are measures of intensity
Copyright © Starsoft Inc, Closing Other things to look at –Mutating dimensions –Hierarchical data (e.g. product structures) –Security –Data Loading –Cleansing –etc.
UNIT-II Principles of dimensional modeling Dimensional modeling: advanced topics ETL OLAP 1.
Data Warehousing M R BRAHMAM. Data Warehousing - Architecture Enterprise Data Warehouse Enterprise Data Warehouse Data Mart Execution Systems CRM ERP.
Data Warehousing DSCI 4103 Dr. Mennecke Introduction and Chapter 1.
Dimensional Modeling CS 543 – Data Warehousing. CS Data Warehousing (Sp ) - Asim LUMS2 From Requirements to Data Models.
Dimensional Modeling. Dimensional Models A denormalized relational model Made up of tables with attributes Relationships defined by keys and foreign keys.
MANAGING DATA RESOURCES ~ pertemuan 7 ~ Oleh: Ir. Abdul Hayat, MTI.
C6 Databases. 2 Problems with traditional file environments Data Redundancy and Inconsistency: –Data redundancy: The presence of duplicate data in multiple.
Intro to MIS – MGS351 Databases and Data Warehouses Chapter 3.
Decision Support and Date Warehouse Jingyi Lu. Outline Decision Support System OLAP vs. OLTP What is Date Warehouse? Dimensional Modeling Extract, Transform,
CHAPTER 11: DIMENSIONAL MODELING: ADVANCED TOPICS.
13 Chapter 13 The Data Warehouse Database Systems: Design, Implementation, and Management, Fifth Edition, Rob and Coronel.
1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) The Data Warehouse Lifecycle Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.
Week 6 Lecture The Data Warehouse Samuel Conn, Asst. Professor.
1 Data Warehouses BUAD/American University Data Warehouses.
Foundations of Business Intelligence: Databases and Information Management.
Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos.
Program Pelatihan Tenaga Infromasi dan Informatika Sistem Informasi Kesehatan Ari Cahyono.
3/6: Data Management, pt. 2 Refresh your memory Relational Data Model Hierarchical & Network Data Model Object-oriented DBMS Designing & Distributing a.
Data Warehouse IMS5024 – presented by Eder Tsang.
CSE6011 Warehouse Models & Operators Data Models relations stars & snowflakes cubes Operators slice & dice roll-up, drill down pivoting.
C6 Databases. 2 Traditional file environment Data Redundancy and Inconsistency: –Data redundancy: The presence of duplicate data in multiple data files.
DIMENSIONAL MODELLING. Overview Clearly understand how the requirements definition determines data design Introduce dimensional modeling and contrast.
MIS2502: Data Analytics The Information Architecture of an Organization.
Data Warehousing DSCI 4103 Dr. Mennecke Chapter 2.
June 08, 2011 How to design a DATA WAREHOUSE Linh Nguyen (Elly)
1 IS 4420 Database Fundamentals Chapter 11: Data Warehousing Leon Chen.
Data Warehousing. Definition Data Warehouse: –A subject-oriented, integrated, time-variant, non- updatable collection of data used in support of management.
Agenda Common terms used in the software of data warehousing and what they mean. Difference between a database and a data warehouse - the difference in.
Copyright © 2016 Pearson Education, Inc. Modern Database Management 12 th Edition Jeff Hoffer, Ramesh Venkataraman, Heikki Topi CHAPTER 9: DATA WAREHOUSING.
Organizing Data & Information Chapter 5. 2 IS for Management Data & Databases Data consists of raw facts that when organized may be transformed into information.
Designing a Data Warehousing System. Overview Business Analysis Process Data Warehousing System Modeling a Data Warehouse Choosing the Grain Establishing.
MIS 385/MBA 664 Systems Implementation with DBMS/ Database Management Dave Salisbury ( )
Ayyat IT Group Murad Faridi Roll NO#2492 Muhammad Waqas Roll NO#2803 Salman Raza Roll NO#2473 Junaid Pervaiz Roll NO#2468 Instructor :- “ Madam Sana Saeed”
5 - 1 Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved.
Dimensional Modeling Business Intelligence Solutions.
1 Data Warehouse Design Architectures Amirkabir University Morteza Zaker Supervisor : Prof. Abbdolahzadeh.
McGraw-Hill/Irwin ©2008,The McGraw-Hill Companies, All Rights Reserved Chapter 5 Data Resource Management.
5-1 McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved.
Data Warehouses and OLAP 1. Review Questions ◦ Question 1: OLAP ◦ Question 2: Data Warehouses ◦ Question 3: Various Terms and Definitions ◦ Question.
SQL Server Analysis Services Understanding Unified Dimension Model (UDM)
Copyright © 2006, Oracle. All rights reserved. Czinkóczki László oktató Using the Oracle Warehouse Builder.
Slide1 4Understand needs and concepts of decision support systems 4Understand concepts and issues of the Data Warehouse 4Understand concepts of On-Line.
What is a database? (a supplement, not a substitute for Chapter 1…) some slides copied/modified from text Collection of Data? Data vs. information Example:
Datawarehousing Concepts | 7.0 9/7/2015 Datawarehousing Concepts.
7.1 Managing Data Resources Chapter 7 Essentials of Management Information Systems, 6e Chapter 7 Managing Data Resources © 2005 by Prentice Hall.
Database Management3-1 L3 Database Management Santa R. Susarapu Ph.D. Student Virginia Commonwealth University.
OLAP Tuning. Outline OLAP 101 – Data warehouse architecture – ROLAP, MOLAP and HOLAP Data Cube – Star Schema and operations – The CUBE operator – Tuning.
Copyright© 2014, Sira Yongchareon Department of Computing, Faculty of Creative Industries and Business Lecturer : Dr. Sira Yongchareon ISCG 6425 Data Warehousing.
Business Intelligence Transparencies 1. ©Pearson Education 2009 Objectives What business intelligence (BI) represents. The technologies associated with.
Introduction to OLAP and Data Warehouse Assoc. Professor Bela Stantic September 2014 Database Systems.
© 2017 SlidePlayer.com Inc. All rights reserved.