The document discusses data warehouses. It begins by defining a data warehouse as a subject oriented, integrated, time-variant, and non-volatile collection of data used to support management decision making. The goals of a data warehouse are to make an organization's information accessible, consistent, adaptive, resilient, and the foundation for decision making. The document then describes the basic elements and architecture of a data warehouse including data marts, OLAP, and how data is extracted, transformed, and loaded. It outlines benefits like increased productivity and competitive advantage as well as potential problems such as underestimating resources and high maintenance needs.

1. Data Warehouse
presented by: Akkal Bist
akkalbist55@gmail.com

2. Agenda
Introduction to data warehouse
The goals of a data warehouse
Type of data warehouse
Architecture of data warehouse
Benefit and problems of data
warehouse

3. Introduction to Data
Warehouse
Subject oriented
Integrated
Time-variant
Non-volatile
Collection of data in support of
management decision making process.

4. The goals of a data
warehouse
 Makes an organization’s information accessible
 Makes the organization’s information consistent
 Is an adaptive & resilient source of information
 Is a secure bastion that protect our information
asset
 Is the foundation for decision making

5. Basic elements of the data warehouse
Storage:
flat files;
RDBMS;
other
Processing:
clean;
prune;
combine;
remove duplicates;
household;
standardize;
conform dimensions;
store awaiting replication;
archive;
export to data marts
No user query services
Data Mart #1:
OLAP (ROLAP or MOLAP)
query services;
dimensional;
subject oriented;
locally implemented;
user group driven;
may store atomic data;
may be frequently refreshed;
conforms to DW bus
Data Mart #2
Data Mart #3
Ad Hoc Query Tools
Report Writers
End User Applications
Models
forecasting;
scoring;
allocating;
data mining;
other downstream systems;
other parameters;
special UI
Source
Systems
(Legacy)
Data Staging Area Presentation Servers End User Data Access
Extract
Extract
Extract
Populate,
replicate,
recover
Populate,
replicate,
recover
Populate,
replicate,
recover
DW
BUS
DW
BUS
Conformed dimensions
Conformed facts
Conformed dimensions
Conformed facts
Feed
Feed
Feed
Feed
upload cleaned
dimensions
Upload model
results

6. Type of data warehouse
1) Data Mart
 A logical subset of the complete data warehouse
 It focused on a single subject (financial area)
 A data warehouse is made up of the union of all
its data marts
 It’s build and controlled by a single department
within an organization
 Data mart can contains not only the summary
data but also atomic data

7. 2) Online Analytical
Processing (OLAP)
 OLAP is a characterized by low volume
transaction
 Query are very complex & involve
aggregation
 It has response time is effective measure
 This system has typically have data latency of
a few hours, as opposed to data marts, where
latency is expected to be closer to one day.

8. OLAP(con..)
Basic Operation
 Roll-up (Consolidation), Drill-down and Slicing
& Dicing
 It characterized by large no of short one-time
transaction(INSERT, UPDATE,DELETE)
 Fast query processing & maintaining data
integrity in multi-access environments
 Measure effectiveness by no of transaction
per second

9. Security in data warehouse
 It is an Integrated repository derived from
multiple source,(OL) databases
 Replication control
 Aggregation and Generalization
 Exaggeration and Mi sleading
 Anonymity

10. Application of data warehouse
It was Micromarketing and profitability
calculation
Stock control
Product category management
Basket analysis
Fraud analysis

11. Architecture

12. Benefits
Potential high return on investment
Competitive advantages
Increased productivity of corporate decision-
making
=> by creating an integrated database of consistent,
subject-oriented, historical data.
More cost-effective decision making
=> help to reduce the over-all cost of product by reducing
the no of channels.

13. Problems
Underestimation of resources of 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 project

14. Thank you!!!