What is online analytical processing (OLAP)?

The concept of online analytical processing (OLAP) was first proposed by the parent of the relational database e.f.codd in 1993, and the proposed OLAP has aroused great repercussions, and OLAP is clearly distinguished from online transaction processing (OLTP) as a class of products.

Today's data processing can be divided into two broad categories: online transaction processing OLTP, online analysis processing OLAP. OLTP is the main application of the traditional relational database, mainly basic, daily transaction processing, such as bank transaction. OLAP is the main application of Data Warehouse system, supports complex analysis operations, focuses on decision support, and provides intuitive and understandable query results.

Online Analytical processing users are professional analysts and management decision-makers in the enterprise, and when they analyze the data of business operation, it is a natural mode of thinking to look at business metrics from different angles. For example, the analysis of sales data, may be integrated time period, product categories, distribution channels, geographical distribution, customer base category and other factors to consider. Although these analysis angles can be reflected by the report, but each analysis angle can generate a report, each analysis angle of different combinations can generate different reports, make IT staff workload is very large, and often difficult to keep up with the pace of management decision-makers thinking.

Good bi products in the online analysis processing, its main feature is directly modeled after the user's multi-angle thinking mode, in advance for the user to form a multidimensional database, here, dimension refers to the user's analysis angle. For example, the analysis of sales data, the time period is a dimension, product categories, distribution channels, geographical distribution, customer base classes are also a dimension. Once the multidimensional data model is established, the user can quickly obtain data from each analysis angle, and can dynamically switch between various angles or perform multi-angle synthesis analysis, which has great analysis flexibility.

We analyze it with business intelligence Finebi. It provides a common OLAP multidimensional Analysis operation, for the user, the existing sample switching dimension can be used for data drilling analysis. At the same time support the data sorting and filtering function, according to their own needs of data analysis processing.

When it comes to data drillthrough analysis, it includes drill up, drill down, slice, dice, and rotate. Drill up is to summarize the low-level detail data to a higher-order summary data, or to reduce the number of dimensions in a certain dimension, while drilling down instead, it goes from summary data into detail data to observe or add new dimensions. Slices and dice are concerned with the distribution of the measurement data on the remaining dimensions after the selected values are on a subset of dimensions. If the remaining dimension is only two, it is a slice, and if there are three, it is cut into pieces. Rotation is the direction of the transformation dimension, which is to rearrange the placement of the dimensions in the table (for example, row and column swaps).

Prototype Cube:

Drill up to drill down slices

Dice rotation

In the context of current big data, the application of business intelligence BI will be the main theme of processing big data in terms of the universality of enterprise-class applications.

What is online analytical processing (OLAP)?