Defining OLAP Solutions and Data Warehouse design
This tutorial covers OLAP solutions used by Data warehouses and understanding Data Warehouse design. The enterprise needs to ask itself certain fundamental questions before actually launching on the process of designing the data warehouse. It must begin with a conviction that a data warehouse would really help its business and the return on investment will make it worth it.
Defining OLAP Solutions
The data warehouse offloads data from a multitude of sources. The cleaned, validated and loaded data is voluminous and daunting. This data needs to be organized, categorized and arranged in meaningful order for analytical purposes. OLAP solutions are specifically designed to cater to this need.
OLAP solutions used by Data warehouses are:
Multidimensional views of data. Data in the data warehouse is organized into subject oriented categories and tables. Fact tables are constructed and linked to various dimensional tables in star or snowflake schemas or combinations of them to form multidimensional views of data. Cubes are built using these multidimensional schemas. Rapid browsing and querying then becomes possible. These views are independent of the way in which data is stored in the data warehouse.
Interactive query and analysis of data is another OLAP solution that enables users drill down, drill up and slice data by using multiple passes. Users can drill down to successive lower levels of detail or roll up to higher levels of summarization and aggregation.
Analytical modeling is an OLAP tool that is a calculation engine for deriving ratios, variances etc., involving measurements and numerical data across many dimensions.
Functional models are made available by using OLAP for forecasting, trend analysis etc. They support users in data analysis.
Graphical OLAP tools are used to display data in 2D or 3D cross tabs and charts and graphs with easy pivoting of axis. This is important for users who need to analyze data from different perspectives and the analysis of one perspective leads to business questions that need to be examined from other perspectives.
Rapid response to queries is a must in any analysis of data and the measure of success for the OLAP tool. Nigel Pendse and Richard Creeth, authors of the OLAP Report developed the FASMI (Fast Analysis of Shared Multidimensional Information) test to judge whether or not an application qualifies to an OLAP tool. Their contention was that an OLAP tool should provide fast browsing capabilities (< five seconds), should contain analytical tools both for the developer and the end user; the cubes must be able to handle the security requirements of sharing confidential information and it should present data multi-dimensionally.
Multi dimensional data storage engine stores data in arrays. These arrays are logical representations of the business dimensions.
Understanding Data Warehouse design
At a very global level, construction of the data warehouse is a business project by itself. The enterprise needs to ask itself certain fundamental questions before actually launching on the process of designing the data warehouse. It must begin with a conviction that a data warehouse would really help its business and the return on investment will make it worth it.
The general questions that are asked may be as below….
Do we need a data warehouse?
What are the kinds of data that are being generated by the enterprise?
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