This document provides an introduction to data warehousing. It defines key concepts like data, databases, information and metadata. It describes problems with heterogeneous data sources and fragmented data management in large enterprises. The solution is a data warehouse, which provides a unified view of data from various sources. A data warehouse is defined as a subject-oriented, integrated collection of historical data used for analysis and decision making. It differs from operational databases in aspects like data volume, volatility, and usage. The document outlines the extract-transform-load process and common architecture of data warehousing.

1. INTRODUCTION TO
DATA WAREHOUSING
By: Eng. Eyad R. Manaa

2. INTRODUCTION
• Data: Meaningful facts, text, graphics, images,
sound, video segments.
• Database: An organized collection of logically
related data.
• Information: Data processed to be useful in
decision making.
• Metadata: Data that describes data.

3. ADVANTAGES OF THE DATABASE APPROACH
• Data Independence/Reduced Maintenance
• Improved Data Sharing
• Increased Application Development Productivity
• Enforcement of Standards
• Improved Data Quality (Constraints)
• Better Data Accessibility/ Responsiveness
• Security, Backup/Recovery, Concurrency

4. PROBLEM:
HETEROGENEOUS INFORMATION SOURCES
“Heterogeneities are
everywhere” Personal
Databases
Digital Libraries
Scientific Databases
World
Wide
Web
 Different interfaces
 Different data representations
 Duplicate and inconsistent information

5. PROBLEM: DATA MANAGEMENT IN LARGE
ENTERPRISES
 fragmentation of informational systems
 Result of application (user)-driven
development of operational systems
Sales Administration Finance Manufacturing ...
Sales Planning
Stock Mngmt
...
Suppliers
...
Debt Mngmt
Num. Control
...
Inventory

6. SOLUTION: UNIFIED ACCESS TO DATA
Integration System
Collects and combines information
Provides integrated view, uniform user interface
Supports sharing
World
Wide
Web
Digital Libraries Scientific Databases
Personal
Databases

7. WHAT IS A DATA WAREHOUSE?
“A data warehouse is simply a single,
complete, and consistent store of data
obtained from a variety of sources and
made available to end users in a way they
can understand and use it in a business
context.”

8. WHAT IS A DATA WAREHOUSE?
“A DW is a
subject-oriented,
integrated,
time-varying,
non-volatile
collection of data that is used primarily in
organizational decision making.”

9. A DATA WAREHOUSE IS
Stored collection of diverse data
A solution to data integration problem
Single repository of information
Subject-oriented
Organized by subject, not by application
Used for analysis, data mining, etc.
Optimized differently from transaction-
oriented db

10. A DATA WAREHOUSE IS
Large volume of data (Gb, Tb)
Non-volatile
Historical
Time attributes are important
Updates infrequent

11. OLTP VS. OLAP
OLTP: On Line Transaction Processing
Describes processing at operational sites
OLAP: On Line Analytical Processing
Describes processing at warehouse

12. WAREHOUSE IS A SPECIALIZED DB
Standard DB (OLTP)
 Mostly updates
 Many small transactions
 Mb - Gb of data
 Current snapshot
 Raw data
 Thousands of users (e.g.,
clerical users)
Warehouse (OLAP)
Mostly reads
Queries are long and complex
Gb - Tb of data
History
Summarized data
Hundreds of users
(e.g., decision-
makers, analysts)

13. GENERIC WAREHOUSE ARCHITECTURE
Extractor/
Monitor
Extractor/
Monitor
Extractor/
Monitor
Integrator
Warehouse
Client Client
Design Phase
Maintenance
Loading
...
Metadata
Optimization
Query & Analysis

14.  ETL Concept
WAREHOUSING PROCESS

15. ETL CONCEPT

16. ETL CONCEPT

17. ISSUES IN DATA WAREHOUSING
Warehouse Design
Extraction
Wrappers, monitors (change detectors)
Integration
Cleansing & merging
Warehousing specification &
Maintenance
Optimizations