OLAP (online analytical processing) allows users to easily extract and view data from different perspectives. It was invented by Edgar Codd in the 1980s and uses multidimensional data structures called cubes to store and analyze data. OLAP utilizes either a multidimensional (MOLAP), relational (ROLAP), or hybrid (HOLAP) approach to store cube data in databases and provide interactive analysis of data.

1. OLAP
Online Analytical Processing
PRESENTED BY:
NURMEEN RAFIQUE
ANIK MALIK
SYED AIENULLAH

2. Includes:
 Introduction
 Invented by
 Olap server and database
 Cubes
 How it works
 Types of OLAP
 Comparison

3. What does mean by Olap
 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 is a powerful technology for data discovery
 OLAP products are typically designed for multiple-user environments
 OLAP applications are widely used by Data Mining techniques

4. Who invented this:
Codd’s 12 Rules for Relational Database Management
 Edgar F. Codd wrote a paper in 1985 defining rules for Relational Database Management
Systems (RDBMS), which revolutionized the IT industry.
 In 1993, Codd and colleagues worked up these 12 rules for defining OLAP (Online Analytical
Processing),
 an industry of software and data processing which allows consolidation and analysis of data in
a multidimensional space.

5. OLAP server and database
OLAP Server
 The chief component of OLAP is the OLAP server,
 which sits between a client and a database management systems (DBMS).
 The OLAP server understands how data is organized in the database and has special
functions for analyzing the data.
 There are OLAP servers available for nearly all the major database systems
OLAP database
 In OLAP database there data, stored in multi-dimensional schemas (usually star schema).
 Data can be imported from existing relational databases to create a multidimensional
database for OLAP.

6. Cubes
 The data structures used in the OLAP are
multidimensional data cubes or OLAP cubes:
 Cube is a data structure that can be imagined as
multi-dimensional spreadsheet.
 Take a spreadsheet, put year on columns,
department on rows – that’s two-dimensional
cube.

7. Facts and Measures
Fact is most detailed information that can be measured.

8. Dimensions
 OLAP is suitable mostly for data which can be
categorized – grouped by categories. The categorical
view of data should be also the main interest of the
data analysis.
 Example of categories might be: color, department,
location or even a date.
 The categories are called dimensions.

9. How it works
 OLAP (online analytical processing) is computer processing that enables a user to easily and
selectively extract and view data from different points of view.
For example,
 a user can request that data be analyzed to display a spreadsheet showing all of a company's
beach ball products sold in Florida in the month of July, compare revenue figures with those for
the same products in September, and then see a comparison of other product sales in Florida in
the same time period.
 To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas
a relational database can be thought of as two-dimensional, a multidimensional database
considers each data attribute (such as product, geographic sales region, and time period) as a
separate "dimension."
 OLAP software can locate the intersection of dimensions (all products sold in the Eastern region
above a certain price during a certain time period) and display them. Attributes such as time
periods can be broken down into sub attributes.

10. Types of OLAP
 Cubes in a data warehouse are stored in three
different modes.
 Multidimensional Online Analytical processing
mode
 Relational Online Analytical Processing mode
 Hybrid Online Analytical Processing mode.
MOLAP
ROLAP
HOLAP

11. MOLAP
 In MOLAP data is stored in form of multidimensional cubes and not in relational databases
 The advantages of this mode is that it provides excellent query performance and the cubes
are built for fast data retrieval.
 All calculations are pre-generated when the cube is created and can be easily applied while
querying data.
 The disadvantages of this model are that it can handle only a limited amount of data

12. ROLAP
 The underlying data in this model is stored in relational databases.
 Since the data is stored in relational databases this model gives the appearance of
traditional OLAP’s slicing and dicing functionality.
 The advantages of this model is it can handle a large amount of data and can leverage
all the functionalities of the relational database.
 The disadvantages are that the performance is slow and each ROLAP report is an SQL
query with all the limitations.

13. HOLAP
 HOLAP technology tries to combine the strengths of the above two
models.
 For summary type information HOLAP leverages cube technology and for
drilling down into details it uses the ROLAP model.

14. Comparing the use of MOLAP and HOLAP
MOLAP
 Cube browsing is fastest when using MOLAP
 MOLAP storage takes up more space as data
is copied and at very low levels of aggregation
 All data is stored in the cube in MOLAP and
data can be viewed even when the original
data source is not available.
ROLAP
 Processing time is slower in ROLAP
 ROLAP takes almost no storage
space as data is not duplicated.
 In ROLAP data cannot be viewed
unless connected to the data source.

15. That’s all