OnLine Analytical Processing

Seminar Presentation By

IQxplorer

Under the guidance of Mr. Indraneel Mukhopadhyay

What is OLAP

OnLine Analytical Processing ia a technology that uses multidimensional view of aggregate data for quicker access to strategic information.

How OLAP Helps

It helps in decision making, business modeling, and operations research activities by transforming raw Data warehouse data into strategic information

Four Main Characteristics of OLAP

–Use multidimensional data analysis techniques

–Provide advanced database support

–Provide easy-to-use end user interfaces

–Support client/server architecture

Multidimensional Data Analysis Techniques

–The processing of data in which data are viewed as part of a multidimensional structure.

–Multidimensional view allows end users to consolidate or aggregate data at different levels.

Multidimensional Data Analysis

Relational databases contains lists of records whose information is organised into fields and is based on a row and column data format(one dimensional).

However some relational tables where there is more than a one-to-one relationship between the fields lends itself to multidiensional represtation.

Product Region Sales

Nuts East 50

Nuts West 60

Nuts Central 100

Bolts East 90

Bolts West 120

Bolts Central 140

Screws East 40

Screws West 70

Screws Central 80

Washers East 20

Washers West 10

Washers Central 30

Single Dimensional View

East West Central

Nuts 50 60 100Bolts 90 120 140Screws 40 70 80Washers 20 10 30

Multidimensional View

OLAP Architecture

–Three Main ModulesOLAP Graphical User Interface (GUI)

OLAP Analytical Processing Logic

OLAP Data Processing Logic

–OLAP systems are designed to use both operational and Data Warehouse data.

OLAP Server Arrangement

OLAP Server With Multidimensional Data Store Arrangement

Relational OLAPRelational On-Line Analytical Processing (ROLAP) provides OLAP functionality by using relational database and familiar relational query tools.

Extensions to RDBMS

Multidimensional data schema support within the RDBMS

Data access language and query performance optimized for multidimensional data

Support for very large databases

Multidimensional Data Schema Support within the RDBMS

Normalization of tables in relational technology is seen as a stumbling block to its use in OLAP systems.

DSS data tend to be non-normalized, duplicated, and pre-aggregated.

ROLAP uses a special design technique to enable RDBMS technology to support multidimensional data representations, known as star schema.

Star schema creates the near equivalent of a multidimensional database schema from the existing relational database

A Typical ROLAP Client/Server Architecture

The real limitation of OLAP databases is almost always the number of cells and not the number of dimensions.

As the number of dimensions increases the number of cells increases expotentially so a 16 dimension database with 5 members in each has 152 billion cells.

Most OLAP servers reach their limit in cell numbers before they hit their dimensions limit

LIMITATIONS

CONCLUSION

In essence OLAP technology is fast, flexible data summarisation and analysis.

OLAP servers are a superior technology for Business Intellgence applications.

OLAP servers and relational databases can work in harmony to create an environment that delivers data quickly to perform the analysis needed to make the best business decisions.