Presentation is loading. Please wait.

Presentation is loading. Please wait.

Module 1: Introduction to Data Warehousing and OLAP

Published byRoderick McKenzie Modified over 8 years ago

Similar presentations

Presentation on theme: "Module 1: Introduction to Data Warehousing and OLAP"— Presentation transcript:

1 Module 1: Introduction to Data Warehousing and OLAP

2 Overview Introducing Data Warehousing Defining OLAP Solutions
Understanding Data Warehouse Design Understanding OLAP Models Applying OLAP Cubes

3 Introducing Data Warehousing
Raw Data vs. Business Information OLTP Source Systems Data Warehouse Characteristics Data Warehouse System Components

4 Raw Data vs. Business Information
Capturing Raw Data Gathering data recorded in everyday operations Deriving Business Information Deriving meaningful information from raw data Turning Data into Information Implementing a decision support system

5 OLTP Source Systems OLTP System Characteristics
Processes real-time transactions of a business Contains data structures optimized for entries and edits Provides limited decision support capabilities OLTP Examples Order tracking Customer service Point-of-sales Service-based sales Banking functions

6 Data Warehouse Characteristics
Provides Data for Business Analysis Processes Integrates Data from Heterogeneous Source Systems Combines Validated Source Data Organizes Data into Non-Volatile, Subject-Specific Groups Stores Data in Structures that Are Optimized for Extraction and Querying

7 Data Warehouse System Components
Data Access User Data Sources Data Marts Data Input Staging Area

8 Defining OLAP Solutions
OLAP Databases Common OLAP Applications Relational Data Marts and OLAP Cubes OLAP in SQL Server 2000

9 OLAP Databases Optimized Schema for Fast User Queries
Robust Calculation Engine for Numeric Analysis Conceptual, Intuitive Data Model Multidimensional View of Data Drill down and drill up Pivot views of data

10 Common OLAP Applications
Executive Information Systems Performance measures Exception reporting Sales/Marketing Applications Booking/billing Product analysis Customer analysis Financial Applications Reporting Planning Analysis Operations Applications Manufacturing Customer service Product cost

11 Relational Data Marts and OLAP Cubes
Data Storage Data Structure N-dimensional Data structure Data Content Detailed and Summarized Data Data Sources Relational and Non-relational Sources Data Retrieval Fast Performance for Data Extract Queries Faster Performance for

12 OLAP in SQL Server 2000 Microsoft Is One of Several OLAP Vendors
Analysis Services Is Bundled with Microsoft SQL Server 2000 Analysis Services Include OLAP engine Data mining technology

13 Understanding Data Warehouse Design
The Star Schema Fact Table Components Dimension Table Characteristics The Snowflake Schema

14 The Star Schema Employee_Dim Dimension Table Fact Table Time_Dim
EmployeeKey EmployeeID ... Dimension Table Fact Table Time_Dim TimeKey TheDate ... Product_Dim ProductKey ProductID ... Sales_Fact TimeKey EmployeeKey ProductKey CustomerKey ShipperKey Sales Amount Unit Sales ... Shipper_Dim ShipperKey ShipperID ... Customer_Dim CustomerKey CustomerID ...

15 Fact Table Components Dimension Tables sales_fact Table
customer_dim Foreign Keys Measures 201 ALFI Alfreds customer_key product_key time_key quantity_sales amount_sales product_dim 201 25 134 400 10,789 Chai /1/2000 time_dim The grain of the sales_fact table is defined by the lowest level of detail stored in each dimension

16 Dimension Table Characteristics
Describes Business Entities Contains Attributes That Provide Context to Numeric Data Presents Data Organized into Hierarchies

17 The Snowflake Schema Defines Hierarchies by Using Multiple Dimension Tables Is More Normalized than a Single Table Dimension Is Supported within Analysis Services

18 Understanding OLAP Models
OLAP Database Components OLAP Dimensions vs. Relational Dimensions Dimension Fundamentals Dimension Family Relationships Cube Measures Relational Data Sources

19 OLAP Database Components
Numeric Measures Dimensions Cubes

20 OLAP Dimensions vs. Relational Dimensions
REGION West East STATE REGION CA West OR West MA East NY East REGION West CA OR East MA NY

21 Dimension Fundamentals

22 Dimension Family Relationships
USA is the parent of North West and South West USA North West Oregon Washington South West California USA North West Oregon Washington South West California USA North West Oregon Washington South West California USA North West Oregon Washington South West California USA North West Oregon Washington South West California USA North West Oregon Washington South West California USA North West Oregon Washington South West California USA North West Oregon Washington South West California North West and South West are children of USA North West and California are descendants of USA North West and USA are ancestors of Washington North West and South West are siblings Oregon and California are cousins All are dimension members

23 Cube Measures Are the Numeric Values of Principle Interest
Correspond to Fact Table Facts Intersect All Dimensions at All Levels Are Aggregated at All Levels of Detail Form a Dimension

24 Relational Data Sources
Star and Snowflake Schemas Are required to build a cube with Analysis Services Fact Table Contains measures Contains keys that join to dimension tables Dimension Tables Must exist in same database as fact table Contain primary keys that identify each member

25 Applying OLAP Cubes Defining a Cube Querying a Cube
Defining a Cube Slice Working with Dimensions and Hierarchies Visualizing Cube Dimensions Connecting to an OLAP Cube

26 Defining a Cube Atlanta Chicago Market Dimension Denver Grapes
Cherries Detroit Melons Products Dimension Apples Q1 Q2 Q3 Q4 Time Dimension

27 Querying a Cube Sales Fact Atlanta Chicago Markets Dimension Denver
Grapes Cherries Dallas Melons Apples Products Dimension Q1 Q2 Q3 Q4 Time Dimension

28 Defining a Cube Slice Atlanta Chicago Markets Dimension Denver Grapes
Cherries Detroit Melons Apples Products Dimension Q1 Q2 Q3 Q4 Time Dimension

29 Working with Dimensions and Hierarchies
Dimensions Allow You to Slice Dice Hierarchies Allow You to Drill Down Drill Up

30 Visualizing Cube Dimensions

31 Connecting to an OLAP Cube

32 Review Introducing Data Warehousing Defining OLAP Solutions
Understanding Data Warehouse Design Understanding OLAP Models Applying OLAP Cubes

Download ppt "Module 1: Introduction to Data Warehousing and OLAP"

Similar presentations

An overview of Data Warehousing and OLAP Technology Presented By Manish Desai.

An overview of Data Warehousing and OLAP Technology Presented By Manish Desai.
Www.ocsinfotech.in OCS Infotech Proprietary & Confidential Typical BI solution Architecture.

OCS Infotech Proprietary & Confidential Typical BI solution Architecture.
Data Warehousing CPS216 Notes 13 Shivnath Babu. 2 Warehousing l Growing industry: $8 billion way back in 1998 l Range from desktop to huge: u Walmart:

Data Warehousing CPS216 Notes 13 Shivnath Babu. 2 Warehousing l Growing industry: $8 billion way back in 1998 l Range from desktop to huge: u Walmart:
Data Warehousing M R BRAHMAM.

Data Warehousing M R BRAHMAM.
2/10/05Salman Azhar: Database Systems1 On-Line Analytical Processing Salman Azhar Warehousing Data Cubes Data Mining These slides use some figures, definitions,

2/10/05Salman Azhar: Database Systems1 On-Line Analytical Processing Salman Azhar Warehousing Data Cubes Data Mining These slides use some figures, definitions,
Exploiting the DW data DW is a platform for creating a wide array of reports It solves data feed problems, but does not lead to specific decision support.

Exploiting the DW data DW is a platform for creating a wide array of reports It solves data feed problems, but does not lead to specific decision support.
Data Sources Data Warehouse Analysis Results Data visualisation Analytical tools OLAP Data Mining Overview of Business Intelligence Data visualisation.

Data Sources Data Warehouse Analysis Results Data visualisation Analytical tools OLAP Data Mining Overview of Business Intelligence Data visualisation.
Advanced Querying OLAP Part 2. Context OLAP systems for supporting decision making. Components: –Dimensions with hierarchies, –Measures, –Aggregation.

Advanced Querying OLAP Part 2. Context OLAP systems for supporting decision making. Components: –Dimensions with hierarchies, –Measures, –Aggregation.
COMP 578 Data Warehousing And OLAP Technology Keith C.C. Chan Department of Computing The Hong Kong Polytechnic University.

COMP 578 Data Warehousing And OLAP Technology Keith C.C. Chan Department of Computing The Hong Kong Polytechnic University.
Www.company.com Lab3 CPIT 440 Data Mining and Warehouse.

Lab3 CPIT 440 Data Mining and Warehouse.
CSE6011 Warehouse Models & Operators  Data Models  relations  stars & snowflakes  cubes  Operators  slice & dice  roll-up, drill down  pivoting.

CSE6011 Warehouse Models & Operators  Data Models  relations  stars & snowflakes  cubes  Operators  slice & dice  roll-up, drill down  pivoting.
Chapter 13 The Data Warehouse

Chapter 13 The Data Warehouse
Introduction to Building a BI Solution 권오주 OLAPForum www.olapforum.com.

Introduction to Building a BI Solution 권오주 OLAPForum
Data Warehousing DSCI 4103 Dr. Mennecke Introduction and Chapter 1.

Data Warehousing DSCI 4103 Dr. Mennecke Introduction and Chapter 1.
Online Analytical Processing (OLAP) Hweichao Lu CS157B-02 Spring 2007.

Online Analytical Processing (OLAP) Hweichao Lu CS157B-02 Spring 2007.
Copying, Managing, and Transforming Data With DTS.

Copying, Managing, and Transforming Data With DTS.
Week 6 Lecture The Data Warehouse Samuel Conn, Asst. Professor

Week 6 Lecture The Data Warehouse Samuel Conn, Asst. Professor
SQL Analysis Services 2005. Microsoft® SQL Server 2005 Analysis Services provides unified, fully integrated views of your business data to support online.

SQL Analysis Services Microsoft® SQL Server 2005 Analysis Services provides unified, fully integrated views of your business data to support online.
Data Warehouse & Data Mining

Data Warehouse & Data Mining
On-Line Analytic Processing Chetan Meshram Class Id:221.

On-Line Analytic Processing Chetan Meshram Class Id:221.

Similar presentations

Ads by Google