degenerate dimension in data warehouse

actual project construction.3) data warehouse modeling method In fact, everything follows its own law. There are also many data warehouse modeling methods. Each modeling method represents a philosophical point of view and a method of generalization and generalization. Currently, there are many popular

, granularity, dimension, measurement value, multi-dimensional data model, and dw2.0. Chapter 3 describes how to design a data warehouse and introduces the concept of metadata. Chapter 4 is the most part of the course class. It took a lot of time to build a Bi system from start to end and finally provided a Web Service

physical model of the Data Warehouse.2. Basic concepts of fact tables and dimension tablesSimply speaking, the dimension table is the angle (dimension) in which you observe the object, and the fact table is what you want to focus on.For example, to analyze the sales of prod

The life cycle approach provides a roadmap for the development of data warehouses, and the overall structure and steps of the life cycle approach are Define Business requirements Technical path Technical Architecture Design Selection and installation of products Data path Dimension modeling Physical design ETL Des

key performance indicators and key business measurement values. They are a way to measure business information in a dimension space. For example, the product income amount, raw material consumption, and replenishment of new employees or equipment running time can all be called indicators. (3) create a star chart based on the information packaging diagram, analyze the detailed category entities, expand it to a snowflake chart, and establish a logical

, organizations, or products ). To help identify theme domains, we need to consider "when, where, who, what, why, and how" related to commercial interests. For example, possible answers to "who" questions include customers, employees, managers, suppliers, business partners and competitors. After identifying the list of all candidate topic domains, you can more clearly break down, rearrange, select, and redefine them to generate a list of theme domains that best represent your customer organizati

project. The data warehouse system is an information delivery platform. It obtains data from the business processing system and organizes data based on Star and snowflake models,It also provides users with various means to obtain information and knowledge from data. In term

be more meaningful to create a repository for data analysis queries. That's what the Data warehouse means. Information from different parts of the system is integrated into the data warehouse for easy access. A cube as a data

One of the previous articles-the Data warehouse multidimensional data model already provides a brief description of the definition and structure of an overly-dimensional model, as well as the concept of the fact table and the dimension table (Dimension table). Multidimension

Label:MySQL Multidimensional Data Warehouse GuideFirst PrinciplesChapter List :Chapter 1: Basic CompositionChapter 2: History of DimensionsChapter 3: Dimensions of additiveChapter 4: Dimension QueriesThis article outlinesYou will use a relational database to implement a dimensional data

warehouse modeling tools and performance tools appear, and the personal experience and quality of designers will play an important role. The implementation of data Warehouse technology at present in the actual application of data warehouse technology mainly includes the fol

Some people often ask a question: do fact tables and dimension tables must be in a database? Why is there such a problem? As we mentioned, if the data warehouse is large enough, it may be split into so-called data mart. Generally, the division is based on the so-called different business modules, such as personnel mana

topics, which can significantly improve the efficiency of the analysis.The Data warehouse is based on the maintenance detail data to process the data, so that it can really be applied to the analysis. Mainly includes three aspects:Aggregation of dataAggregated data here ref

Basic concepts: 1. Multi-dimensional dataset: a multi-dimensional dataset is the main object in Online Analytical Processing (OLAP) and a technology that allows quick access to data in a data warehouse. A multi-dimensional dataset is a collection of data. It is usually constructed from a subset of a

Ix. Degradation DimensionsThis section discusses a technique called a degenerate dimension. This technology reduces the number of dimensions and simplifies the dimension Data Warehouse model. Simple patterns are easier to understand than complex and have better query perform

different locations or users. The web-based information publishing system is the most effective way to deal with multi-user access. A data warehouse is generated in order to further explore data resources and make decisions when a large number of databases exist. It is by no means a "large database ". The emergence of da