Defining Data Warehouse Concepts and Terminology

Slides:



Advertisements
Similar presentations
Business Information Warehouse Business Information Warehouse.
Advertisements

Chapter 13 The Data Warehouse
Supporting End-User Access
By: Mr Hashem Alaidaros MIS 211 Lecture 4 Title: Data Base Management System.
Data Warehousing M R BRAHMAM.
Data Warehouse Architecture Sakthi Angappamudali Data Architect, The Oregon State University, Corvallis 16 th May, 2005.
Data Warehouse IMS5024 – presented by Eder Tsang.
Introduction to Data Warehousing. From DBMS to Decision Support DBMSs widely used to maintain transactional data Attempts to use of these data for analysis,
1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) The Data Warehouse Lifecycle Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.
13 Chapter 13 The Data Warehouse Hachim Haddouti.
Chapter 13 The Data Warehouse
Introduction to Building a BI Solution 권오주 OLAPForum
Data Warehousing DSCI 4103 Dr. Mennecke Introduction and Chapter 1.
Data Warehousing: Defined and Its Applications Pete Johnson April 2002.
Defining Data Warehouse Concepts and Terminology.
Basic Concepts of Datawarehousing An Overview Prasanth Gurram.
Week 6 Lecture The Data Warehouse Samuel Conn, Asst. Professor
Intro to MIS – MGS351 Databases and Data Warehouses Chapter 3.
Data Warehouse & Data Mining
Database Systems – Data Warehousing
Data Warehouse Concepts Transparencies
DW-1: Introduction to Data Warehousing. Overview What is Database What Is Data Warehousing Data Marts and Data Warehouses The Data Warehousing Process.
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie.
I Information Systems Technology Ross Malaga 4 "Part I Understanding Information Systems Technology" Copyright © 2005 Prentice Hall, Inc. 4-1 DATABASE.
Data warehousing and online analytical processing- Ref Chap 4) By Asst Prof. Muhammad Amir Alam.
1 Data Warehouses BUAD/American University Data Warehouses.
2 Copyright © Oracle Corporation, All rights reserved. Defining Data Warehouse Concepts and Terminology.
OLAP & DSS SUPPORT IN DATA WAREHOUSE By - Pooja Sinha Kaushalya Bakde.
1 Reviewing Data Warehouse Basics. Lessons 1.Reviewing Data Warehouse Basics 2.Defining the Business and Logical Models 3.Creating the Dimensional Model.
5 - 1 Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved.
Decision Support and Date Warehouse Jingyi Lu. Outline Decision Support System OLAP vs. OLTP What is Date Warehouse? Dimensional Modeling Extract, Transform,
6.1 © 2010 by Prentice Hall 6 Chapter Foundations of Business Intelligence: Databases and Information Management.
Sachin Goel (68) Manav Mudgal (69) Piyush Samsukha (76) Rachit Singhal (82) Richa Somvanshi (85) Sahar ( )
Data Warehouse. Group 5 Kacie Johnson Summer Bird Washington Farver Jonathan Wright Mike Muchane.
Data Warehouses and OLAP Data Management Dennis Volemi D61/70384/2009 Judy Mwangoe D61/73260/2009 Jeremy Ndirangu D61/75216/2009.
Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide
 Understand the basic definitions and concepts of data warehouses  Describe data warehouse architectures (high level).  Describe the processes used.
Chapter 5 DATA WAREHOUSING Study Sections 5.2, 5.3, 5.5, Pages: & Snowflake schema.
DATA RESOURCE MANAGEMENT
Business Intelligence Transparencies 1. ©Pearson Education 2009 Objectives What business intelligence (BI) represents. The technologies associated with.
Advanced Database Concepts
Oracle 8i Data Warehousing (chapter 1, 2) Data Warehousing Lab. 석사 1 학기 HyunSuk Jung.
1 Copyright © Oracle Corporation, All rights reserved. Business Intelligence and Data Warehousing.
The Need for Data Analysis 2 Managers track daily transactions to evaluate how the business is performing Strategies should be developed to meet organizational.
Copyright © 2006, Oracle. All rights reserved. Czinkóczki László oktató Using the Oracle Warehouse Builder.
Data Warehouse – Your Key to Success. Data Warehouse A data warehouse is a  subject-oriented  Integrated  Time-variant  Non-volatile  Restructure.
C Copyright © 2007, Oracle. All rights reserved. Introduction to Data Warehousing Fundamentals.
2 Copyright © 2006, Oracle. All rights reserved. Defining Data Warehouse Concepts and Terminology.
BUSINESS INTELLIGENCE. The new technology for understanding the past & predicting the future … BI is broad category of technologies that allows for gathering,
Data Mining and Data Warehousing: Concepts and Techniques What is a Data Warehouse? Data Warehouse vs. other systems, OLTP vs. OLAP Conceptual Modeling.
Business Intelligence Overview
Intro to MIS – MGS351 Databases and Data Warehouses
Defining Data Warehouse Concepts and Terminology
Data warehouse.
Data warehouse and OLAP
Chapter 13 The Data Warehouse
Data Warehouse—Subject‐Oriented
Data Warehousing and Data Mining By N.Gopinath AP/CSE
Datamining : Refers to extracting or mining knowledge from large amounts of data Applications : Market Analysis Fraud Detection Customer Retention Production.
Databases and Data Warehouses Chapter 3
Defining Data Warehouse Concepts and Terminology
Data Warehouse and OLAP
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie
Supporting End-User Access
Introduction of Week 9 Return assignment 5-2
Data Warehouse.
Data Warehouse Overview September 28, 2012 presented by Terry Bilskie
Data Warehousing Concepts
Data Warehouse and OLAP
Data Warehouse and OLAP Technology
Presentation transcript:

Defining Data Warehouse Concepts and Terminology Chapter 3

Definition of a Data Warehouse “ An enterprise structured repository of subject-oriented, time-variant, historical data used for information retrieval and decision support. The data warehouse stores atomic and summary data.” Oracle Data Warehouse Method

Data Warehouse Properties Subject Oriented Integrated Data Warehouse Non Volatile Time Variant

Subject-Oriented Data is categorized and stored by business subject rather than by application OLTP Applications Data Warehouse Subject Equity Plans Customer financial information Shares Insurance Savings Loans

Integrated Data on a given subject is defined and stored once. Savings Current accounts Loans Customer OLTP Applications Data Warehouse

Time-Variant Data is stored as a series of snapshots, each representing a period of time

Nonvolatile Typically data in the data warehouse is not updated or delelted. Operational Warehouse Load Insert Update Delete Read Read

Changing Data First time load Warehouse Database Operational Refresh

Data Warehouse Versus OLTP Property Operational Data Warehouse Response Time Sub seconds to seconds Seconds to hours Operations DML Primarily read only Nature of Data 30-60 days Snapshots over time Subject, time Data Organization Applications Size Small to large Large to very large Operational, Internal, External Data Source Operational, Internal Activities Processes Analysis

Usage Curves Operational system is predictable Data warehouse - Variable - Random

User Expectations Control expectations Set achievable targets for query response Set SLAs Educate Growth and use is exponential

Enterprisewide Warehouse Large scale implementation Scope the entire business Data from all subject areas Developed incrementally Single source of enterprisewide data Single distribution point to dependent data marts

Data Warehouses Versus Data Marts

Dependent Data Mart Flat Files Marketing Operational Systems Marketing Sales Finance Human Resources Marketing Marketing Data Warehouse Data Marts External Data

Independent Data Mart Flat Files Operational Systems Sale or Marketing External Data

Data Warehouse Terminology Operational data store (ODS) Stores tactical data from production systems that are subject-oriented and integrated to address operational needs Metadata Metadata

Data Warehouse Terminology Enterprise data warehouse Architecture Business area warehouse Data Integration Source data

Methodolgy Ensures a successful data warehouse Encourages incremental development Provides a staged approach to an enterprisewide warehouse - Safe - Manageable - Proven - Recommended

Modeling Warehouses differ from operational structures: - Analytical requirements - Subject orientation Data must map to subject oriented information: - Identify business subjects - Define relationships between subjects - Name the attributes of each subject Modeling is iterative Modeling tools are available

Extraction, Transformation, and Transportation OLTP Databases Staging File Warehouse Database Purchase specialist tools, or develop programs Extraction-- select data using different methods Transformation--validate, clean, integrate, and time stamp data Transportation--move data into the warehouse

Data Management Efficient database server and management tools for all aspects of data management Imperatives - Productive - Flexible - Robust - Efficient Hardware, operating system and network management

Data Access and Reporting Simple Queries Forecasting Warehouse Database Drill-down Tools that retrieve data for business analysis Imperatives - Ease of use - Intuitive - Metadata - Training More than one tool may be required

Oracle Warehouse Components Any Source Any Data Any Access Relational / Multidimensional Text, image Spatial Web Audio video Relational tools Operational data OLAP tools External data Applications/Web

Oracle Data Mart Designer Oracle Data Mart Suite Data Modeling Oracle Data Mart Designer OLTP Databases Data Mart Database Ware- housing Engines OLTP Engines SQL*Plus Data Extraction Oracle Data Mart Builder Data Management Oracle Enterprise Manager Data Access & Analysis Discoverer & Oracle Reports

Data Mart Implementation with the Oracle Data Mart Suite Oracle Enterprise Server Oracle Enterprise Manager Oracle Data Mart Builder Oracle Data Mart Designer Oracle Discoverer Oracle Web Application Server Oracle Reports

Oracle Warehouse Builder Architecture Extraction Facilities Loader Remotes SQL Gateways - OLE-DB/ODBC - Mainframe - Specialized ERP Data - SAP - Peoplesoft - Oracle Sources PL/SQL, Java Transforms Target Tables Transform Driver Filter Transform PL/SQL, Java Wrapper Oracle 8i External Functions

Oracle Business Intelligence Tools IS develops user’s Views Business users Analysis Current Tactical Strategic Oracle Reports Oracle Discover Oracle Express

The Tool for Each Task Question Tool Task Production reporting Ad hoc query and analysis Advanced What were sales by region last quarter? Oracle Reports What is driving the increase in North American sales? Oracle Discover Given the rapid increase in Web sales, what will total sales be for the rest of the year? Oracle Express

Oracle Warehouse Services Education Oracle Consulting Customers Oracle Support Services

Summary This lesson covered the following topics: Identifying a common, broadly accepted definition of the data warehouse Distinguishing the differences between OLTP systems and analytical systems Defining some of the common data warehouse terminology Identifying some of the elements and processes in a data warehouse Identifying and positioning the Oracle Warehouse vision, products, and services