What is OLAP?

What is OLAP?

Asked on November 13, 2018 in Database.
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    • To enable a user to easily and selectively extract/ view data from different points of view, we use OLAP (online analytical processing), a computer processing.

    • For example, in the month of July, a user can request that data be analyzed to display a spreadsheet showing all of a company’s beach ball products sold in India, in September compare revenue figures with those for the same products , and then see a comparison of other product sales in India in the same time period.

    • To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. A multidimensional database considers each data attribute as a separate “dimension”, thus a relational database can be two-dimensional.

    • 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(like time periods) can be broken down into subattributes.

    • For data mining or the discovery of previously undiscerned relationships between data items, OLAP can be used. An OLAP database does not need to be as large as a data warehouse, since not all transactional data is needed for trend analysis.

    • Data can be imported from existing relational databases to create a multidimensional database for OLAP, using Open Database Connectivity (ODBC).

    • Two leading OLAP products are Hyperion Solution’s Essbase and Oracle’s Express Server. OLAP products are typically designed based on the cost of the softwareand also based on the number of users.

    Answered on November 13, 2018.
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    OLAP is an acronym for Online Analytical Processing. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. It is the foundation for many kinds of business applications for Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting. OLAP enables end-users to perform ad hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making.

    Answered on January 14, 2019.
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    OLAP (online analytical processing) is a computing method that enables users to easily and selectively extract and query data in order to analyze it from different points of view. OLAP business intelligence queries often aid in trends analysis, financial reporting, sales forecasting, budgeting and other planning purposes.

    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.

    Analysts can then perform five types of OLAP analytical operations against these multidimensional databases:

    • Roll-up. Also known as consolidation, or drill-up, this operation summarizes the data along the dimension.
    • Drill-down. This allows analysts to navigate deeper among the dimensions of data, for example drilling down from “time period” to “years” and “months” to chart sales growth for a product.
    • Slice. This enables an analyst to take one level of information for display, such as “sales in 2017.”
    • Dice. This allows an analyst to select data from multiple dimensions to analyze, such as “sales of blue beach balls in Iowa in 2017.”
    • Pivot. Analysts can gain a new view of data by rotating the data axes of the cube.
    Answered on January 14, 2019.
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    To facilitate this kind of analysis, data is collected from multiple data sources and stored in data warehouses then cleansed and organized into data cubes. Each OLAP cubecontains data categorized by dimensions (such as customers, geographic sales region and time period) derived by dimensional tables in the data warehouses. Dimensions are then populated by members (such as customer names, countries and months) that are organized hierarchically. OLAP cubes are often pre-summarized across dimensions to drastically improve query time over relational databases.

    Analysts can then perform five types of OLAP analytical operations against these multidimensional databases:

    • Roll-up. Also known as consolidation, or drill-up, this operation summarizes the data along the dimension.
    • Drill-down. This allows analysts to navigate deeper among the dimensions of data, for example drilling down from “time period” to “years” and “months” to chart sales growth for a product.
    • Slice. This enables an analyst to take one level of information for display, such as “sales in 2017.”
    • Dice. This allows an analyst to select data from multiple dimensions to analyze, such as “sales of blue beach balls in Iowa in 2017.”
    • Pivot. Analysts can gain a new view of data by rotating the data axes of the cube.

    OLAP software then locates the intersection of dimensions, such as all products sold in the Eastern region above a certain price during a certain time period, and displays them. The result is the “measure”; each OLAP cube has at least one to perhaps hundreds of measures, which are derived from information stored in fact tables in the data warehouse.

    Answered on February 20, 2019.
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