Download Data Warehousing

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Clusterpoint wikipedia, lookup

Big data wikipedia, lookup

Data Protection Act, 2012 wikipedia, lookup

Data model wikipedia, lookup

Data center wikipedia, lookup

Forecasting wikipedia, lookup

Database model wikipedia, lookup

Data analysis wikipedia, lookup

Information privacy law wikipedia, lookup

3D optical data storage wikipedia, lookup

Data vault modeling wikipedia, lookup

Business intelligence wikipedia, lookup

Data Warehousing
Text and Resources
The Data Warehouse Lifecycle Toolkit,
Kimball, Reeves, Ross, and Thornthwaite
Internet resources
Data Warehousing Institute
Teradata Institute
Intelligent Enterprise
Data Warehouse Approach
An old idea with a new interest:
Cheap Computing Power
Special Purpose Hardware
New Data Structures
Intelligent Software
Heightened Business Competition
Data Warehouse
“Queryable source of data in the enterprise”
Common source of consistent
organizational information
Identify problems and opportunities
User focused
Retrieval focused
Goals of the Course
Understand the Data Warehouse philosophy
Dimensional modeling
Tools for Warehouse management
Business intelligence
Business practice
What To Expect
Help develop course expectations for the
Two tests
Exercises and a semester project
Graduate Presentation
What is a data warehouse?
A database filled with
large volumes of
historical business
information that users
can access with
various query tools.
The warehouse usually
resides on its own
server and is separate
from the transactionprocessing or “runthe-business” systems.
Purpose of a data warehouse
Provides an architecture for the flow of data
from operational systems to decision
support systems
DW involves a many record analysis, during
which all data has to be locked
Used to discover trends and patterns
Present opportunities
 Identify problems
ROI of data warehouses
New insights into
Customer habits
Developing new products
Selling more products
Cost savings and revenue
Cross-selling of products
Less mainframe computer
Identify and target most
profitable customers
Capital outlay and
development/training time
can be extraordinary.
Quality of system output
Levels of risk
Intangibles (middle ground)
Course Outline
Introduction and basic principles
Data extraction: SQL data definition code
Warehouse Architecture: Dimensional
Data Cleansing: SAS Datastep coding
Data Presentation: MS Analysis Services