The document presents information on data warehousing. It defines a data warehouse as a repository for integrating enterprise data for analysis and decision making. It describes the key components, including operational data sources, an operational data store, and end-user access tools. It also outlines the processes of extracting, cleaning, transforming, loading and accessing the data, as well as common management tools. Data marts are discussed as focused subsets of a data warehouse tailored for a specific department.

1. PRESENTATION
ON
DATA
WAREHOUSING
Presented By:
Jagnesh Chawla
Manpreet Singh
Mintu

2. CONTENTS:
 Meaning Of data warehousing
 Benefit of data warehousing
 Problems
 Architecture of data warehouse
 Main components
 Data flows
 Tools and technologies
 Data Mart

3. MEANING:
 Data warehouse is data management and data
analysis
 Goal: is to integrate enterprise wide corporate
data into a single reository from which users can
easily run queries

4. BENEFITS:
 The major benefit of data warehousing are high
returns on investment.
 Increased productivity of corporate decision-
makers

5. PROBLEMS:
 Underestimation of resources for data loading
 Hidden problems with source systems
 Required data not captured
 Increased end-user demands
 Data homogenization
 High demand for resources
 Data ownership
 High maintenance
 Long-duration projects
 Complexity of integration

6. ARCHITECTURE:
Operational Reporting, query,
data source1 application
development,
High and EIS(executive
Meta-data summarized data information system)
Operational Query Manage
tools
data source 2 Lightly
Load Manager summarized
data
Operational
data source n Detailed data DBMS
OLAP(online analytical
processing) tools
Operational
Warehouse Manager
data store (ods)
ational data store (ODS)
Data mining
Archive/backup
data
End-user
Typical architecture of a data warehouse access tools

7. MAIN COMPONENTS:
 Operational data sourcesfor the DW is
supplied from mainframe operational data held in
first generation hierarchical and network databases,
departmental data held in proprietary file systems,
private data held on workstaions and private serves
and external systems such as the Internet,
commercially available DB, or DB assoicated with
and organization’s suppliers or customers
 Operational datastore(ODS)is a
repository of current and integrated operational data
used for analysis. It is often structured and supplied
with data in the same way as the data warehouse, but
may in fact simply act as a staging area for data to be
moved into the warehouse

8. MAIN COMPONENTS:
 query manageralso called backend
component, it performs all the operations
associated with the management of user queries.
The operations performed by this component
include directing queries to the appropriate
tables and scheduling the execution of queries
 end-user access toolscan be categorized into
five main groups: data reporting and query tools,
application development tools, executive
information system (EIS) tools, online analytical
processing (OLAP) tools, and data mining tools

9. DATA FLOW:
 Inflow- The processes associated with the
extraction, cleansing, and loading of the data
from the source systems into the data warehouse.
 upflow- The process associated with adding value
to the data in the warehouse through
summarizing, packaging , packaging, and
distribution of the data
 downflow- The processes associated with
archiving and backing-up of data in the
warehouse

10. DATA FLOW:
 outflow- The process associated with making the
data availabe to the end-users.
 Meta-flow- The processes associated with the
management of the meta-data

11. Warehouse Manager
Operational
data source1
Meta-flow
Meta-data High
summarized data
Inflow Outflow
Lightly
Load summarized
data
Manager
Upflow Query Manage
Operational
DBMS
data source n Detailed data
Warehouse Manager
Operational
data store (ods)
Data mining
tools
End-user
Downflow access tools
Archive/backup
data
Information flows of a data warehouse

12. TOOLS AND TECHNOLOGIES:
 The critical steps in the construction of a data
warehouse:
a. Extraction
b. Cleansing
c. Transformation

13. TOOLS AND TECHNOLOGIES:
 after the critical steps, loading the results into
target system can be carried out either by
separate products, or by a single, categories:
 code generators
 database data replication tools
 dynamic transformation engines

14. MANAGEMENT TOOLS:
 For the various types of meta-data and the day-
to-day operations of the data warehouse, the
administration and management tools must be
capable of supporting those tasks:
 Monitoring data loading from multiple sources
 Data quality and integrity checks
 Managing and updating meta-data
 Monitoring database performance to ensure efficient query
response times and resource utilization

15.  Auditing data warehouse usage to provide user
chargeback information
 Replicating, subsetting, and distributing data
 Maintaining effient data storage management
 Purging data;
 Archiving and backing-up data
 Implementing recovery following failure
 Security management

16. DATA MART:
 Data mart a subset of a data warehouse that
supports the requirements of particular
department or business function
 The characteristics that differentiate data marts
and data warehouses include:
 A data mart focuses on only the requirements of
users associated with one department or business
function

17. Warehouse Manager
Operational
data source1
High
Meta-data
summarized data
Operational
data source 2 Lightly Query
Load summarized
data Manage
Manager
Operational
DBMS
Detailed data
data source n
Warehouse Manager
Operational
data store (ods)
(First Tier)
(Third Tier)
Operational data store
(ODS)
Archive/backup End-user
data access tools
Data Mart
summarized
data(Relational database)
Summarized data
(Multi-dimension database) (Second Tier)
Typical data warehouse adn data mart architecture

18. DATA MART ISSUES:
 Data mart functionalitythe capabilities of data marts
have increased with the growth in their popularity
 Data mart sizethe performance deteriorates as data
marts grow in size, so need to reduce the size of data marts
to gain improvements in performance
 Data mart load performancetwo critical components:
end-user response time and data loading performanceto
increment DB updating so that only cells affected by the
change are updated and not the entire MDDB structure

19. REFERENCES:
 Book of DBMS
 Google.com
 Wikipedia, the free encyclopedia
 InformIT.com
 Allfree-stuff.com