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Whether it is an enterprise information system or a Web site, various sizesProgramThe original functions are data operations. It can be seen that a group's various requirements for some data have created one program after another, or software systems. Looking back, we started to deal with data from the very beginning. What should we do first when a new project
industry, so there will be different companies targeting a specific industry model. For different industries, the same company also has different models, depending on the different business characteristics of different industries. For example, the tdwm model of IBM contains the following nine concepts: Figure 1. tdwm conceptual model of IBM Many may ask why your model has nine concepts, not ten, and 11? What is the basis of your data warehouse model
Discussion on data modeling methods in data warehouse construction The main content of this article is not to introduce some data models of the existing popular main industries, but to share some of my experiences in
Https://edu.hellobi.com/course/54High-quality Database modeling 1-significance1. The concept and significance of the data modeldikw--(data) information (information) knowledge (knowledge) wisdom (Wisdom), I understand that information is obtained through data, knowledge is gained through information, and wisdom is gene
business dimension information for integrated integration, this situation is more appropriate for generating surrogate keys to master keys. Summary The construction of the dimension table seems relatively simple, in most cases the business library will be directly, but in addition to the different levels of the dimension of Redundancy (Star model), but also need to grasp the details of the following dimensions of the construction of attention, after all, the dimension of errors will
0x00 Preface The following content, is the author in the study and work of some summary, of which the concept of most of the content from the book, the practical content mostly from their own work and personal understanding. Due to the lack of qualifications, there will inevitably be many mistakes, I hope to criticize. Overview The Data warehouse contains a lot of content, which can include architecture, modeling
This article was reproduced from: http://coolshell.cn/articles/7270.html ================================================ Full-text translation from the wall of the article "NoSQL Data Modeling techniques", translation is not good, please forgive me. After reading this article, you may feel a little bit about NOSQL data structures. My feeling is that the relation
I recently found a good book on Data Modeling in a bookstore-data modeler's workbench: tools and techniques for analysis and design ), author Steve Hoberman. After a rough review, I feel that this book is indeed worthy of the praise of the translators and foreign experts: "This book is full of techniques and Techniques useful for improving
Ho, August See a colleague on the desktop there is a Data Warehouse Toolbox Third edition, this blog simply discusses the Data Warehouse modeling general process and modeling methods (mainly practical experience and network data integration) First describe the application
to design the HBase primary key in the HBase authoritative guide. There is no methodology like programming design patterns that can be followed at the model architecture level. When comparing different NoSQL databases, various metrics, such as extensibility, performance, and consistency, are often used in addition to features. Since these indicators are often used in the original sense of NoSQL, they are deeply studied from a theoretical point of view and a practical point of view, and the basi
Notes | data The recent bookstore has found a good book for data Modeling-data modeling: tools and techniques for analyzing and designing (Workbench:tools and techniques for analytics and design), by Steve Hoberman. After rough, it feels like the book is truly worthy of the
Brief introduction The business environment is changing rapidly, and so is the type of business data. A successful Data warehouse solution is based on flexible design that adapts to changing business data. The architecture of data Warehouse and the modeling of warehouse
operation, such as sending an email. It is used to analyze data and discover new values from existing data. It is mainly used to predict future situations. A data warehouse is not a general structure for solving all problems. It must be concentrated in a certain problem area, such as aviation services and customer benefits. The
Tags: des Lucene style blog http io ar os useOriginal from " NoSQL Data Modeling techniques ", compiled by the cool shell net Chenhao NoSQL Data Modeling Technology ". After reading this article, you may feel a little bit about NOSQL data structures. My feeling is that the r
IntroductionThe evaluation of several modeling tools was highlighted in the previous blog, and SQL Power architect was selected as the final modeling tool, and the Oracle Data Modeler support Vertica was also studied during the evaluation process, although the eventual abandonment of Oracle data Modeler, but the scalab
1. Define the mining target To understand the real needs of users, to determine the target of data mining, and to achieve the desired results after the establishment of the model, by understanding the relevant industry field, familiar with the background knowledge. 2. Data acquisition and processing of clear mining objectives, the need to extract from the business data
The author from 98 into the field of database and data warehousing has been nearly eight years of time, the data modeling work contact more, innovative dare not talk about, this article will be working experience summed up for everyone to discuss and correct. To bring up data mode
For information technology applications such as data integration, process modeling is a method that has been tried and proved feasible. The problem of virtualization and standardization is also covered by modeling technologies for data integration application processes. First, let's look at the types of process
this chapter, we will introduce the main content of feature engineering, focusing on the main content of data cleansing and data feature preprocessing, including data cleansing, feature acquisition, feature processing (include pointing, normalization, normalization, etc.), feature dimensionality reduction and feature derivation. The quality of pretreatment direc
regression method, backward regression method, stepwise regression method4. The steps of linear regression analysis (1) To do the basic analysis of the data, the analysis is the potential of the interpretation of the variables and the underlying relationship between the variables to be interpreted;(2) The candidate model can be constructed according to the results of preliminary analysis;(3) To test the validity hypothesis of candidate model;(4) to d
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