OLAP, that isOnline Analytical ProcessingBi brings a brand new way of data observation and is one of the core technologies of Bi. We know that data is stored in a database as a data table. For example, the sales data of a store is stored in a data table as follows:
Sales time |
Sales location |
Product |
Sales quantity |
Sales amount |
2004-11-1 |
Beijing |
Soap |
10 |
342.00 |
2004-11-6 |
Guangzhou |
Orange |
30 |
123.00 |
2004-12-3 |
Beijing |
Bananas |
20 |
12.00 |
2004-12-13 |
Shanghai |
Orange |
50 |
189.00 |
2005-1-8 |
Beijing |
Soap |
10 |
342.00 |
2005-1-23 |
Shanghai |
Toothbrush |
30 |
150.00 |
|
Guangzhou |
Toothbrush |
20 |
100.00 |
Policy makers often want to know macro information such as distribution, proportions, and trends, such as the following: > Is the sales volume in Beijing time changing? > Which product has the largest increase in sales in 2005 compared with that in 2004? > What is the proportional distribution of product sales in 2004? ...... In the face of such requirements, a large number of sum operations must be performed using SQL statements. Each time a problem is obtained, SQL sum is required. In the face of the above seven records, we can easily produce results, but when we face millions or even hundreds of millions of records, such as mobile company call data, each SQL sum operation consumes a lot of time for calculation. The decision makers often propose analysis requirements on the first day and wait until the second day to obtain the calculation results. This analysis method is "offline analysis ", low efficiency. To improve data analysis efficiency, OLAP completely breaks the record-based data browsing mode, and separates data into dimensions and measures )": >DimensionIs to observe the data angle, such as the above example of "sales time", "sales location", "product "; >MeasurementIs the specific quantity value, such as the "sales quantity" and "sales amount" in the previous example "; In this way, we can convert the data list of the previous normal version into a cube with three dimensions ):
The process of data exploration is to determine a point in the cube, and then observe the measurement value of this point:
Of course, the data cube is not limited to three dimensions. Here we use three dimensions to illustrate the problem, just because the limit shown by the graph is three dimensions. Dimensions can be divided into layers. For example, the time can be summarized from day to day as month and year, the product can be summarized as food and daily necessities, and the location can be summarized as North China and South China, you can drill down or roll up at any level of the dimension ):
In this way, we can get rid of the speed constraints of SQL sum, quickly locate detailed data that meets different conditions, and quickly obtain summary data at a certain level. OLAP technology provides a multi-angle, multi-level, and efficient data exploration method for decision makers. The thinking of decision makers is no longer limited by fixed drop-down menus and query conditions, instead, the decision maker's thinking leads the data acquisition and any combination of analysis perspectives and analysis objectives. This breaks the traditional Interactive Analysis and high efficiency and makes OLAP the core application of the Bi system. It is an OLAP analysis interface provided by Cognos powerplay. You only need to drag the dimensions and measurements you are interested in to the corresponding positions to obtain charts and reports:
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