OLAPOnline Analytical Processing

PRESENTED BY:NURMEEN RAFIQUEANIK MALIKSYED AIENULLAH

Includes:

Introduction Invented by Olap server and database Cubes How it works Types of OLAP Comparison

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

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. 

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.

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.

Facts and Measures

Fact is most detailed information that can be measured.

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.

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.

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. 

MOLAPROLAPHOLAP

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

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.

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.  

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.  

That’s all