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The Kimball of Master Ralph: The Inmon of Bill master 1. Brief introduction The architecture design of BI system is the core of Data Warehouse architecture design. In the field of Data Warehouse architecture, there are two schools of theoretical knowledge, which were presented by two Masters Ralph Kimballbill Inmo
Label:In short, the database is a transaction-oriented design, the Data Warehouse is a theme-oriented design.Databases generally store online transaction data, and data warehouses typically store historical data.Database design is to avoid redundancy as far as possible, generally adopt the rule of conforming to the pat
Data ZT: A brief explanation of common nouns in data Warehouse Data Warehouse in the middle of the 80 's, Mr. William H.inmon, the "Father of the warehouse", defined the concept of data
Top 10 best practices for building a large Relational Data Warehouse Writer:Stuart ozer, Prem Mehra, and Kevin Cox Technical ReviewPerson:Lubor Kollar, Thomas kejser, Denny Lee, Jimmy may, Michael Redman, and Sanjay Mishra Building a large relational data warehouse is a complex task. This article describes some design
documents that may be generated outside the site and other types of data that are useful for company decisions. data storage: The source data is exported through the daily tasks of ETL, and is converted into the Data warehouse in the form of attributes. The
, respectively: (1) Information Model of Data Warehouse (DW), such as Data Warehouse dimension table and fact table; (2) System Management information model, such as: System User table, User rights table, etc.; (3) Operational data
through the dimension table, like snowflakes.For example: Figure 1: Star model Figure 2 Snowflake Model In terms of performance, Star Model query performance is good. To improve performance, you can allow violations of the third paradigm, appropriate redundancy, and hidden constraints between tables. Dimension Modeling The business dimension is merged into th
performance is good.To improve performance, you can allow violations of the third paradigm, proper redundancy, and the hiding of constraints between tablesDimension modelingThe business dimension is fused into the data model, and the name dimension is modeled.Or, for analysis convenience (commercial application requirements), the different levels of the same
The common dwh architecture is as simple as figures 2 and 3. Generally, for an enterprise, the data lifecycle is 5-7 years, especially for detailed data. The lower the data granularity level, the shorter the lifecycle, the higher the data granularity and the longer the lifecycle. For the flow account
Tags: Data Warehouse optimizationin the absence of a real data warehouse database, now all data warehouses are just a relational database created based on the dimension model, but the Data
warehouse and some features of the OLTP system, It is a "theme-oriented, integrated, current, or near-current, changing" data. ---from Baidu Encyclopedia In fact, most of the time, ODS just acts as a temporary data storage, data buffering role. Generally speaking After the data
Label:In the absence of a real data warehouse database, all data warehouses are now just a relational database created based on the dimension model, but the Data Warehouse database itself has some differences, such as the unique f
EndorsementWith the advent of big data, often hear the relevant vocabulary, dimensions, indicators, BI, PV, UV, and so on, if you do not know what the meaning, embarrassed to come out mixed, today I tidied up these words, can let everyone quickly into the relevant topics.DwDW is the abbreviation for Data Warehouse, which is the database
The topic of today's blog is: parameter linkage solution. Environment: Data Warehouse + OLAP (cube) + Reporting Service. Effect: multiple parameters (1, 2 ,......) Achieve interaction. First, let's talk about the principle. A data warehouse consists of fact tables and dimensions. I understand that fact tables are actua
Objective:Ready to systematize a set of distributed Data Warehouse Modeling Practice Guide, the first list of the table, is to design a goal for themselves.The first part of the basic articleChapter One concept and definition of data Warehouse1.1 Data Management System1.2 Data
One-network fund settlement analysis system for Guangdong expressway Based on the Data Warehouse Star Model Magenfeng(Guangdong Union Electronic Service Co., Ltd., Guangzhou 510300, China) SummaryThe star mode is a well-known concept in the Data Warehouse Based on relational databases. the design concept of the star co
example:Figure 1: Star-shaped modelFigure 2 Snowflake ModelFrom the performance point of view, Star Model query performance is good.To improve performance, you can allow for violations of the third paradigm, appropriate redundancy, and hiding the constraints between tables.Dimension modelingThe business dimension is fused into the data model, and the name dimension
Data Warehouse different levels have different granularity, DW layer is the data is atomic granularity of data, such as transaction data atomic granularity is an order, records also include the purchase of users and merchants, DM layer
quality. Therefore, data Warehouse system construction needs to realize the interaction between data and application. Data Warehouse requirements for data quality The requirements of data
Tags: targe LAN causes HTTP extensions three-month partial PST calculationIn short, the database is a transaction-oriented design, the Data Warehouse is a theme-oriented design.Databases generally store online transaction data, and data warehouses typically store historical data.Database design is to avoid redundancy a
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