A data warehouse is a subject-oriented, integrated collection of data from multiple sources used to support management decision making. It contains cleansed and integrated data stored using a common data model. Online analytical processing (OLAP) allows users to analyze and view data from different perspectives using multidimensional views, calculations, and time intelligence functions. OLAP applications are commonly used for financial modeling, sales forecasting, and other business analyses.

1. Dr. Panjabrao Deshmukh Polytechnic,
Amravati
Presented By :
Ku. Devyani B.Vaidya
Guided By :
Prof. R.H.Rathod

2. •The data can be stored in many different types of
databases. One data base architecture that has recently
emerged is the “data warehouse”, a repository of
multiple heterogeneous data sources. Data warehouse
technology includes data cleansing, data integration and
online Analytical processing
•OLAP stands for analysis techniques with
functionalities such as summarization, consolidation and
aggregation, as well as the ability to view information
from different angles.

3. •A data warehouse is a subject-oriented, integrated, time-
variant and non-volatile collection of data in support of
management’s decision making process. So, data
warehouse can be said to be a semantically consistent data
store that serves as a physical implementation of a decision
support data model and stores the information on which an
enterprise needs to make strategic decisions.

5. •Enterprise warehouse
•Data mart
•Virtual warehouse

6. •Present the organization's information consistently.
•Provide a single common data model for all data of
interest regardless of the data's source.
•Restructure the data so that it makes sense to the
business users.

7. •OLAP (online analytical processing) is computer
processing that enables a user to easily and selectively
extract and view data from different points of view.
•OLAP allows users to analyze database information from
multiple database systems at one time.
•OLAP data is stored in multidimensional databases.

8. •The term OLAP was created as a slight modification
of the traditional database term OLTP (Online
Transaction Processing).
•Databases configured for OLAP employ a
multidimensional data model, allowing for complex
analytical and ad‐hoc queries with a rapid execution
time.
•They borrow aspects of navigational databases and
hierarchical databases that are speedier than their
relational kind.

9. •An OLAP Cube is a data structure that allows fast
analysis of data.
•The arrangement of data into cubes overcomes a
limitation of relational databases.
•It consists of numeric facts called measures which are
categorized by dimensions.
•The OLAP cube consists of numeric facts called
measures which are categorized by dimensions.

10. •A multidimensional cube can combine data from
disparate data sources and store the information in
a fashion that is logical for business users.

12. •Slice
•Dice
•Drill Down/Up
•Roll‐up
•Pivot

13. 1. Multidimensional views of data
2. Calculation-intensive capabilities
3. Time Intelligence

14. OLAP applications are found in the area of financial
modeling (budgeting, planning), sales forecasting,
customer and product profitability, exception reporting,
resource allocation, variance analysis, promotion planning,
and market share analysis
•A data warehouse is a subject-oriented, integrated, time-
variant and non volatile collection of data in support of
management’s decision making process.
Data warehousing is the process of constructing and using
data warehouses. Data warehousing is very useful from the
point of view of heterogeneous database integration.