Online Analytical Processing is used to answer the complex queries posted on data warehouse. In order to solve the queries of nature ‘who?’ and ‘what?’ we can use the simple tools but to answer the advanced queries like ‘what if?’ and ‘why?’, we require special tool that can support online analytical processing (OLAP).
Online analytical processing (OLAP) is defined as “The dynamic synthesis, analysis, and consolidation large volumes of multi-dimensional data.”
OLAP is a term that describes a technology that uses a multi-dimensional view of aggregate data to provide quick access to strategic information for the purposes of advanced analysis. OLAP enables users to gain a deeper understanding and knowledge about various aspects of their corporate data through fast, consistent, interactive access to a wide variety of possible views of the data.
OLAP enables decision-making about future actions. Atypical OLAP calculation can be more complex than simply aggregating data, for example, ‘What would be the effect on property sales in the different regions of Punjab if legal costs went up by 3.5% and Government taxes went down by 1.5% for properties over Rs 100,000’.
Analytical Queries per Minute (AQM) is used as a standard benchmark for comparison of performances of different OLAP tools. OLAP systems should as much possible hide users from the syntax of complex queries and provide consistent response times for all queries no matter how complex.
We’ll be covering the following topics in this tutorial:
Features of OLAP
There are the following key features of OLAP:
• Multi-dimensional views of data;
• Support for complex calculations;
• Time intelligence
Multi-dimensional views of data
A multi-dimensional view of data provides the basis for analytical processing through flexible access to corporate data. It enables users to analyze data across any dimension at any level of aggregation with equal functionality and ease.
Support for complex calculations
OLAP software must provide a range of powerful computational methods such as that required by sales forecasting such as moving averages and percentage growth.
Time intelligence is used to judge the performance of almost any analytical application over time. For example, this month versus last month or this month versus the same month last year or a user may require to view, the sales of the month of Mayor the sales for the first five months of 2007. Concepts such as year-to-date and period-over-period comparisons should be easily defined in an OLAP system.