OlapCube

Short Description:
OlapCube is a simple, yet powerful tool to analyze data. OlapCube will let you create local cube files (with .cub extension) from data stored in any relational database. You can explore the resulting cube with our OlapCube Reader or Microsoft Excel.

Long Description 1:
OlapCube is a simple, yet powerful tool to analyze data. OlapCube will let you create local cubes (files with .cub extension) from data stored in any relational database (including MySQL, PostgreSQL, Microsoft Access, SQL Server, SQL Server Express, Oracle, Oracle Express). You can explore the resulting cube with our OlapCube Reader. Or you can use Microsoft Excel to create rich and customized reports.

Long Description 2:
OlapCube is a simple, yet powerful tool to analyze data. OlapCube will let you create local cubes (files with .cub extension) from data stored in any relational database (including MySQL, PostgreSQL, Microsoft Access, SQL aServer, SQL Server Express, Oracle, Oracle Express). You can explore the resulting cube with our OlapCube Reader. Or you can use Microsoft Excel to create rich and customized reports. The OLAP cube consists of numeric facts called measures which are categorized by dimensions. An OLAP cube is a data structure that allows fast analysis of data. The arrangement of data into cubes overcomes a limitation of relational databases. Relational databases are not well suited for near instantaneous analysis and display of large amounts of data. Instead, they are better suited for creating records from a series of transactions. Although many report-writing tools exist for relational databases, these are slow when the whole database must be summarized. OLAP cubes can be thought of as extensions to the two-dimensional array of a spreadsheet. For example a company might wish to analyze some financial data by product, by time-period, by city, by type of revenue and cost, and by comparing actual data with a budget. These additional methods of analyzing the data are known as dimensions. A financial analyst might want to view or 'pivot' the data in various ways, such as displaying all the cities down the page and all the products across a page. This could be for a specified period, version and type of expenditure Each of the elements of a dimension could be summarized using a hierarchy. The hierarchy is a series of parent-child relationships, typically where a parent member represents the consolidation of the members which are its children. Parent members can be further aggregated as the children of another parent. For example May 2005 could be summarized into Second Quarter 2005 which in turn would be summarized in the Year 2005.