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This article mainly tells you briefly what is mysql multidimensional data warehouse. You can look up the relevant professional terms on the Internet or find some related books to supplement them. We will not dabble here. Let's go straight to the topic. I hope this article on what is mysql multidimensional data warehouse can bring you some practical help.

Data warehouse can achieve the unity of information by integrating a variety of data, including the current transaction operation and management information system, as well as a variety of external information sources. The source data will be integrated, cleaned, transformed, and if the data needs to be read directly from these sources, the data will be stored in a more operationally friendly manner in the data warehouse.

The data structure of the data warehouse allows you to store current and historical data. Current data is necessary for actual transaction activities, usually regular hard copies (such as printed reports) or online reports. Historical data, on the other hand, are often not so easy to use and can provide business information based on point-in-time analysis, such as tracking, inferential analysis, and comparison, which are important for long-term planning and strategic market decisions.

How to integrate information from multiple information sources, regular accumulation and storage, effective requirements design and development technologies, all of which are very different from the technologies used in transactional management information systems. This book is all about the design and development technology of data warehouse, it covers most of the technical issues involved in the establishment of data warehouse. More importantly, this book provides an easy-to-understand guide for the development of an actual data warehouse.

Scope of application of this book

The mysql Multidimensional data Warehouse Guide is a hands-on book. You will use the mysql database, but the book is not about mysql. This book does not deal with any hardware architecture issues.

This book is mainly concerned with the design and development technology of data warehouse. It does not involve related technical issues such as project management, theory, and how to lead development.

This book uses an example of data warehouse development to show how technology is applied. Provide data model and sql script, will be able to apply to the actual data warehouse development. These scripts are already using mysql 5 on the Windows xp professional sp2 platform. 0 . Version 21 passed the test.

In addition, the following topics are not specifically discussed in this book:

The concept of data warehouse

Sql

MySQL database

People who are suitable for reading this book:

Data warehouses are used in a variety of organizations and business organizations, from government departments, non-profit organizations to schools, from manufacturing to retail stores, from financial institutions to medical institutions, from traditional companies to Internet merchants.

This book is first for data warehouse developers. However, it managers and other it professions, especially those interested in mis (business reporting) and dss (decision support applications), will find this book equally useful. In general, this book is for those who are related to preparing data for analytical applications, as well as those who need to submit information, such as printed reports and online reports.

This book is also applicable to beginners in data warehouses. It will directly and quickly help those who are preparing to develop their first data warehouse.

Teachers and students can use the book as a textbook to clarify their understanding of data warehouse principles and concepts. Most of the chapters can be customized for lab exercises.

Preparatory skills

This book is not for it newcomers, in order to make more effective use of this book, readers must have some system development experience. However, prior experience in data warehouse construction is not required.

People who need to practice the examples in this book need to have practical skills in rdbms (Relational Database Management system) and sql.

You can get it from this book.

You will be able to hone your data warehouse knowledge and practical skills with just an example, a data warehouse that first stores data related to commercial sales, and practices. This example is a stripped-down version of the actual data warehouse, and its prototype can be found in many business types.

You will develop the relevant data warehouse in this example step by step based on the mysql database using the techniques described in this book. These techniques are the decomposition skills of the problems that will be encountered in the development of data warehouse. By completing this article and completing all the exercises, you will gain relevant work experience and be ready to take charge of the first actual data warehouse project.

Chapter overview

This book contains 25 chapters and an appendix. All chapters are organized into four parts. The first part covers the foundation of data warehouse. The second part describes the migration from raw data to data warehouse. The third part discusses how to control the development and evolution of data warehouse. The fourth part deals with some advanced multi-dimensional technologies. The next section gives a preview of each chapter.

The first part of the basic principles

The first article, which covers the fundamentals of multidimensional data warehousing, has four chapters.

Chapter 1, "basic composition," introduces the star schema (a database schema with a fact table surrounded by multiple dimension tables) and explains the basic composition of the schema.

Chapter 2, "Dimension History", introduces the use of agent keys to maintain the historical records of dimensional members.

Chapter 3, "measure additivity," contains one of the most important features of a dimensional data warehouse, namely, the additivity of measures stored in the fact table of the data warehouse.

Chapter 4, "Dimension query", introduces a kind of sql query which is most suitable for star schema. Dimension query can be used to prove whether a dimensional data warehouse has two basic design indicators: simplicity and efficiency.

Part 2: extract, transform and load

All five chapters of this article cover data integration, fact tables, and dimension tables.

Chapter 5, "Source data extraction", introduces the extraction of different types of data.

Chapter 6, "importing time Dimensions", covers the three most general techniques for loading time dimensions.

Chapter 7, "initialization Import" and Chapter 8, "Periodic Import" involve two types of import techniques, initialization and periodicity.

Chapter 9, "cycle Import Plan," as a summary of the second article, provides an advanced guide on how to use Windows's task manager to implement periodic import plans.

The third part: growth

The third part introduces different processing technologies, which mainly deal with the related problems encountered in the growth of a successful multidimensional data warehouse. This part has ten chapters.

Chapter 10, "adding Fields," discusses the technical issues of adding a field to a table in an existing data warehouse.

Chapter 11, "loading on demand", deals with the technology of loading on demand.

Chapter 12, "dimension table subset", introduces related techniques to help users deal with the problem of dimension table subset.

Chapter 13, "Dimension role-playing," is about using the same dimension multiple times in a fact table.

Chapter 14, "Snapshots", allows you to provide fast performance queries when you need to make summary data.

Chapter 15, "Weidi hierarchy" and Chapter 16, "Multi-path and uneven Dimension hierarchy" are about simple and multi-path dimensional techniques, which, accordingly, help people to analyze convergence and drilling.

Chapter 17, "Dimension degradation", shows how to use dimension degradation techniques to reduce the complexity of a data warehouse pattern.

Chapter 18, "garbage dimension", is about the technology of garbage dimension, that is, data that is ostensibly unrelated but often required by users to be analyzed are organized in a dimensional way.

Chapter 19, "Multi-star patterns," shows how to add multiple star patterns to a pattern.

Part IV: advanced Technology

It contains six chapters.

Chapter 20, "uneven data sources," describes how to deal with data sources in a data warehouse whose structures cannot be mapped directly to the target table.

Chapter 21, "fact tables without facts", helps you create auxiliary data for your customers to analyze, a fact table without fact fields that cannot be measured directly from the data source.

Chapter 22, "belated facts", contains a technique for dealing with situations where a particular fact in the data source does not occur before the planned loading time.

Chapter 23, "external data sources and Dimension merging," includes two topics: dealing with external data sources and techniques for merging scattered attributes in multiple dimensions into one dimension.

Chapter 24, "Cumulative Metrics", discusses two related issues: the inadditivity of computational metrics and cumulative metrics.

Chapter 25, "Segmentation Dimension", describes a technique that can help users analyze data for attributes with continuous values.

Appendix

Appendix a, "format file data source", describes how to use the format file data source in the examples of this book.

Mysql, the most popular open source database software for data warehouse applications, has never been introduced as an introductory guide to the creation of multidimensional data warehouses before this book. Topics include star schema modeling, data loading (data extraction, transformation, and loading: etl), test validation, and multidimensional queries. This book applies a practical and relatively concise real project from beginning to end. Its comprehensive and acceptable form of writing allows programmers who need to build a data warehouse to get relevant examples and materials.

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