The document summarizes a data warehouse project. It includes the coordinator, group members, quarter, and technologies used in data warehousing like Microsoft, Oracle, IBM and SAP. It defines what a data warehouse is and its components. Key terms used in data warehousing are also defined, including subject oriented, integrated, nonvolatile and time-variant. Principles of building, managing and delivering a data warehouse are discussed. The document also summarizes Microsoft SQL Server 2005 and its capabilities for data warehousing like manageability, scalability and interactive data access. It discusses how Microsoft is working to reduce complexity and costs associated with data storage.

1. Coordinator : Nguyen Tien Cuong
Group : 3
Student : Nguyen Tuan Anh
Do Thi Hoa
Nguyen Nam Hoang
Nguyen Duy Hai
Quarter : 2

2.  Microsoft
 Oracle
 IBM
 SAP

4. A data warehouse is a relational
database that is designed for query
and analysis rather than for
transaction processing. It usually
contains historical data derived from
transaction data, but can include data
from other sources
Data warehouses separate analysis
workload from transaction workload
and enable an organization to
consolidate data from several
sources.
In addition to a relational database, a
data warehouse environment can
include an
extraction, transportation, transforma
tion, and loading (ETL)
solution, online analytical processing
(OLAP) and data mining
capabilities, client analysis tools, and
other applications that manage the
process of gathering data and
delivering it to business users

5. These terms are used as follows
Subject Oriented: The data in the data
warehouse is organized so that all the
data elements relating to the same real -
world event or object are linked together.
Integrated: Though the data in the data
warehouses is scattered around different
tables, databases or even servers but the
data is integrated consistently in the
values of variables, naming conventions
and physical data definitions.
Nonvolatile: Data in the data warehouse
is never over-written or deleted - once
committed, the data is static, read -
only, and retained for future reporting.
Time – variant: The changes to the data
in the data warehouse are tracked and
recorded so that reports can be produced
showing changes over time.

6. A data warehouse must develop a process to collect the pure raw materials
and then continually repackage them to serve evolving business initiatives.
Data warehouses must be built, managed, and delivered. We do not want to
change the technology so it’s important to get it right.
A long feature set is not beneficial if development is onerous and cycle
turnaround times are long and costly.
Each new initiative that a data warehouse serves should be treated as a
project within a program.
The data warehouse process is an information product process. We must
establish the guiding principles and champion, architect, deliver, and support
iterations.
The data warehouse engine is the company’s information factory and it
should have high reliability.

7. Microsoft SQL Server 2005 provides a
manageable, scalable data warehouse platform.
With several enhancements in SQL Server
2005, Microsoft enables Information Technology
departments to productively manaSge their
growing data volumes along with the rapid
increase in usage of the data warehouse.
SQL Server database management system
Accessible techniques to manage deployment
Delivering a data warehouse
Interactive data access
SQL Server Analysis Services
Reporting Services

8. Through the technology underlying the Microsoft Data
Warehousing Framework and the remarkable progress in
Microsoft SQL Server 7.0, Microsoft is working to reduce
complexity, improve integration, and lower costs
associated with storage data.
Customers investing in data storage technology based on
Microsoft can be assured that they are creating
applications with the best possible economic
considerations, while maintaining full scalability and
reliability of their systems.