TIỂU LUẬN MÔN CƠ SƠ DỮ LIỆU BUILDING SMALL DATA WAREHOUSE

BUILDING SMALL DATA
BUILDING SMALL DATA
WAREHOUSE
WAREHOUSE
Giảng viên hướng dẫn: Nguyễn Hà Nam.
Sinh viên thực hiện: Nguyễn Đức Bình.
Phùng Thị Hương.
Content
Content

What is Data Warehouse.

Dimensional Modeling.

Data Warehouse Architecture.

Building Data Warehouse for Puncia
What is Data Warehouse?
What is Data Warehouse?

Introdution about data warehouse.

“A data warehouse is a subject-oriented,
integrated, time-variant, and nonvolatile collection
of data in support of management’s decision-
making process.”—W. H. Inmon.
What is Data Warehouse?
What is Data Warehouse?
Data Warehouse—Subject Oriented
Data Warehouse—Subject Oriented


Data is organized in major objects or business
processes.

Example of subject oriented data are customer,
product, vendor and sale transaction.
Data Warehouse—Integrated
Data Warehouse—Integrated
Data Warehouse — time variant
Data Warehouse — time variant
Data Warehouse — nonvolatile
Data Warehouse — nonvolatile
Dimensional Modeling
Dimensional Modeling

Is a database design technique to support
business users to query data in data warehouse.

There are two important concepts: facts and
dimensions.

The dimensional data model is built based on
star schema.
Conceptual Modeling of Data Warehouses
Conceptual Modeling of Data Warehouses

Star schema: A fact table in the middle
connected to a set of dimension tables

Snowflake schema: A refinement of star
schema where some dimensional

hierarchy is normalized into a set of
smaller dimension tables, forming a
shape similar to snowflake

Fact constellations: Multiple fact tables
share dimension tables, viewed as a
collection of stars, therefore called
galaxy schema or fact constellation
Example of Star Schema
Example of Star Schema
Example of Snowflake schema
Example of Snowflake schema
Example of Fact constellations
Example of Fact constellations
Popular Data Warehouse Architectures
Popular Data Warehouse Architectures

Enterprise data warehouse architecture

Dimensional data warehouse architecture

Federated data warehouse architecture

Data Mart
Enterprise data warehouse architecture
Enterprise data warehouse architecture
Dimensional data warehouse architecture
Dimensional data warehouse architecture
Kimball vs Inmon
Kimball vs Inmon


Bill Inmon recommends to build data
warehouse that follows top down
approach. In Inmon’s philosophy, it is
starting with building a big centralized
enterprise data warehouse.

Ralph Kimball recommends to build data
warehouse that follows bottom up
approach. It is first start with mission
critical data marts that serve analytic
needs of departments. Then it is
integrating these data marts for data
consistency through a so called
information bus.
Kimball vs Inmon
Kimball vs Inmon
Data Warehouse For Punica
Data Warehouse For Punica

Introduction Punica Company and Punica’s OLTP

Reporting requirements punica

Building Data warehouse with windows 7, SQL
server 2008.
Introduction Punica
Introduction Punica

Is company domain name owner punica.vn.


Provide services broker, customer through 03
site.
◦
Broker_network by Drupal.
◦
Site for customer by Drupal.
◦
Forum by Vbulletin.
Interface for broker site
Interface for broker site
Interface for customer site
Interface for customer site
Reporting requirements punica
Reporting requirements punica

The number of postings each week, month,
quarter With group user, location, demand &
type for real estate.

Average Price for real estate with time, location,
type for real estate.
Punica – Subject Area
Punica – Subject Area
Choose architecture approach for building
Choose architecture approach for building

Data integration requirements

Skill sets


Time constraint

Cost to build
Dimensional data warehouse architecture