MySQL Multidimensional Data Warehouse Guide is a practical book. You will use the MySQL database, but this book is not about MySQL. This book does not cover any hardware architecture issues. This book is mainly concerned with the design and development technology of data warehouse. Welcome to mysql multidimensional data warehouse guide.
Recommended course: MySQL tutorial
Data warehouse can unify information by integrating various data sources. Including current business operations and management information systems, as well as various external information sources. These source data will be integrated, cleaned, transformed, and if data needs to be read directly from these data sources, the data will be stored in a data warehouse in a more operationally friendly manner.
The data warehouse's data structure allows you to store current and historical data. Current data is necessary for actual transaction operations activities and is typically a periodic hard copy (such as a printed report) or an online report. Historical data, which is often not so easy to use, can provide business information based on point-in-time analysis, such as tracking, inferential analysis, and comparison, which are all important for long-term planning and strategic market decisions.
How to integrate information from multiple information sources, regularly accumulate storage, effective demand design and development techniques, all of which are very different from the techniques used in transactional management information systems. This book is all about data warehouse design and development techniques, and it covers most of the technical issues involved in building a data warehouse. More importantly, this book provides an easy-to-understand how-to guide for developing a practical data warehouse.
Scope of application of this book
MySQL Multidimensional Data Warehouse Guide is a practical book. You will use the MySQL database, but this book is not about MySQL. This book does not cover any hardware architecture issues.
This book is mainly concerned with the design and development technology of data warehouse. It does not involve the management of development projects, theory, and related technical issues such as how to lead development.
This book uses an example of data warehouse development to show how the technology is applied. Provide data models and sql scripts, which will be suitable for actual data warehouse development. These scripts have been tested on the Windows xp professional sp2 platform using mysql version 5.0.21.
In addition, this book will no longer discuss the following topics in particular:
The concept of data warehouse
Sql
MySQL database
For people reading this book:
Data warehouses are used in a variety of organizations and businesses, from government departments and non-profits to schools, from manufacturing to retail stores, from financial institutions to healthcare Institutions, from traditional companies to Internet merchants.
This book is primarily intended for data warehouse developers. However, IT managers and other IT professionals, especially those interested in MIS (business reporting) and DSS (decision support applications), will find this book equally useful. In general, this book is intended for those who are involved in preparing data for analytical applications, and those who need to submit information, such as printed reports and online reports.
This book is also suitable for data warehouse beginners. It will provide immediate and immediate assistance to those who are preparing to develop their first data warehouse.
Teachers and students can use this book as a textbook to clarify their understanding of data warehouse principles and concepts. Most chapters can be customized for laboratory exercises.
Prepared Skills
This book is not for IT newcomers. In order to use this book more effectively, readers must have some system development experience. However, prior experience in data warehouse construction is not required.
Those who need to practice the examples in this book need to have practical skills in RDBMS (relational database management system) and SQL.
What you can get from this book
You will be able to hone your data using just an example, a data warehouse that originally stored business sales-related data, and practice it. Warehouse knowledge and practical skills. This example is a stripped-down version of an actual data warehouse, prototypes of which can be found in many business types.
You will use the techniques introduced in this book to develop the sales-related data warehouse in this example step by step based on the MySQL database. These techniques are decomposition techniques for problems that will be encountered in common data warehouse development. By completing this article and completing all the exercises, you will gain relevant work experience and be prepared to take charge of your first real-world data warehouse project.
Chapter Overview
This book contains 25 chapters and an appendix. All chapters are organized into four parts. The first part covers data warehousing basics. The second part describes the migration from raw data to the data warehouse. Part 3 discusses how to control the evolution of data warehouses. The fourth part involves some advanced multidimensional techniques. The following sections give a preview of each chapter.
Part 1 Basic Principles
Part 1 covers the basic principles of multidimensional data warehouse and has four chapters.
Chapter 1, "Basic Components", introduces the star schema (a database schema with a fact table surrounded by multiple dimension tables) and explains the basic components of the schema.
Chapter 2, "Dimension History" introduces the use of proxy keys to maintain historical records of dimension members.
Chapter 3, "Measure Additivity," covers one of the most important features of dimensional data warehouses, which is the additivity of measures stored in data warehouse fact tables.
Chapter 4, "Dimension Query", introduces a SQL query that is most suitable for star schema. Dimension query can be used to prove whether a dimensional data warehouse has the two most basic design indicators: Simple and efficient.
Part 2: Extraction, Transformation and Loading
All 5 chapters of this article involve 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 common techniques for loading time dimensions.
Chapter 7, "Initial Import" and Chapter 8, "Periodic Import" involve two types of import techniques, initial and periodic.
Chapter 9, "Periodic Import Plan", as a summary of the second article, provides an advanced guide on how to use the Windows Task Manager to implement a regular import plan.
Part 3: Growth
Part 3 introduces different processing technologies, which mainly deal with related problems encountered in the growth process of a successful multi-dimensional data warehouse. This part has ten chapters.
Chapter 10, "Adding a Field" discusses the technical issues of adding a field to a table in an existing data warehouse.
Chapter 11, "On-demand loading" involves on-demand loading technology.
Chapter 12, "Dimension Table Subset", introduces related technologies to help users deal with dimension table subset problems.
Chapter 13, "Dimension Role Playing", about using the same dimension multiple times in a fact table.
Chapter 14, "Snapshot" allows you to provide fast performance queries when you need to summarize data.
Chapter 15, "Dimension-only Hierarchies" and Chapter 16, "Multiple Paths and Ragged Dimension Hierarchies" are about simple and multipath dimensional techniques that, accordingly, help people aggregate and drill analyze.
Chapter 17, "Dimensionality Reduction," shows how to use dimensionality reduction techniques to reduce the complexity of a data warehouse schema.
Chapter 18, "Garbage Dimension", is about the technology of garbage dimension, that is, data that is seemingly irrelevant but is often required to be analyzed by users is organized into dimensions.
Chapter 19, "Multiple Star Schemas" shows how to add multiple star schemas to the schema.
Part 4: Advanced Technology
Contains six chapters.
Chapter 20, "Incomplete Data Sources", introduces how to deal with data sources in the data warehouse whose structures cannot be directly mapped to the target table.
Chapter 21, "Fact Table without Facts", helps you create a kind of auxiliary data for customers to analyze, a fact table without fact fields. This data cannot be measured directly from the data source. .
Chapter 22, "Late Facts," contains a technique for handling situations when a particular fact in the data source does not occur before the planned load time.
Chapter 23, "External Data Sources and Dimension Merging", includes two topics: techniques for processing external data sources and how to merge scattered attributes in multiple dimensions into one dimension.
Chapter 24, "Cumulative Measures," discusses two related issues: the nonadditivity of computational measures and cumulative measures.
Chapter 25, "Segmented Dimension", describes a technique that can help users perform data analysis on attributes with continuous values.
Appendix
Appendix a, "Format File Data Source", introduces how to use the format file data source in the examples in this book.
Using Mysql, the most popular open source database software, for data warehouse applications has never been introduced in this introductory guide to creating a multidimensional data warehouse before this book. Topics include star schema modeling, data loading (data extraction, transformation, and loading: etl), test validation, and multidimensional queries. This book uses a practical, relatively simple, real project from beginning to end. Its comprehensive, accessible writing format makes relevant examples and information available to programmers who need to build data warehouses.
About the author
Djoni Darmawikarta once worked for IBM and is now a technical expert in the data warehouse and business intelligence team of a Canadian insurance company. Currently living in Toronto, Ontario
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