Data modeling technology of Business Intelligence BI

Business applications 、 Data integration 、 Data management 、 Both data warehouse and machine learning have a common and essential component ： Data model .

Almost every critical business solution is based on a data model . It could be an online transaction or a point of sale 、 Finance 、 Product and customer management 、 Business intelligence or Internet of things , No suitable data model , The value of business data is zero ！

Since the beginning of the computer age , Data models and data modeling methods already exist . For the foreseeable future , The data model will remain the foundation of the business application . In the field of data modeling , Developed the basic knowledge of mapping complex business models . In order to successfully model the data , It is particularly important to understand the basic principles and relationships between the various topics and reproduce them with examples . Data needs a structure , Without it , It doesn't make any sense , Nor can the computer process it as bits and bytes .

 

What is business intelligence , Why is it important ？

 

The concept of business intelligence first appeared in 1960 years . business intelligence , Also known as BI, Business analysis 、 data mining 、 Data Infrastructure 、 A general term or common term for data visualization and various sub fields of data tools . All in all ,BI Analyze all data generated by the enterprise , And make reports 、 Performance metrics and trends , To help management make decisions .

 

When optimizing business processes and positioning for future success ,BI crucial . because BI Our goal is to provide you with company data in all areas of the company , Therefore, it can be used to improve the efficiency and productivity of the company and respond to market changes . With business intelligence , You can identify and evaluate data and ultimately react to achieve goals .

 

Data modeling technology —— summary

 

The following is an overview of various data modeling techniques ：

• • Flat data model ： In this very simple database model , All data is in a two-dimensional table , It consists of columns and rows . Assume that columns have values of similar types , And in the row , Elements should have relationship values with each other .

 

• • Layered model ： Data is stored in a tree structure . The data is stored in a root or top-level directory that contains various other directories and files .

 

• • A network model ： This model is very similar to the hierarchical model , But the hierarchical tree is replaced by the graph . In this model , Record interconnections , They are allocated through linked tables . In this way , Maintain hierarchies between records .

 

• • relational model ： The model represents the database as a collection of relationships . A relationship is just a table of values . A set of predicates with fixed predicate variables , Its possible values or combinations are limited .

 

• • Star pattern model ： A star schema is a database framework Model , One of the fact tables references multiple dimension tables , Optimized for use in data warehouse or business intelligence .

 

• • Data vault model ： Entries with long-term storage history data from various data sources , These entries are arranged in the hub 、 In and related to the satellite and link tables . Its core is a modern 、 An agile way to design and build efficient 、 An effective data warehouse .

banq： What's the latest DDD Domain modeling ：

Domain events are the breakthrough entrance to business process analysis and design

 

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