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mysqlworkbench的教程
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更新于2023-06-15
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关于mysql的workbench的一些教程,版本是mysqlbench5.2的,希望大家看清楚了在下载,谢谢!
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1
MySQL Workbench
Database Design. Development. Administration. Migration.
A MySQL
®
White Paper
Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
2
Table of Contents
1! INTRODUCTION..................................................................................................................... 3!
2! TYPES OF DATA TO MANAGE ............................................................................................ 3!
3! MODEL-DRIVEN DATA MANAGEMENT .............................................................................. 4!
4! MODEL-DRIVEN DATA MANAGEMENT BENEFITS ........................................................... 4!
5! MYSQL WORKBENCH – DATA MODELING/DESIGN FOR MYSQL................................... 7!
6! MYSQL WORKBENCH: SQL DEVELOPMENT .................................................................. 11!
7! MYSQL WORKBENCH: ADMINISTRATION....................................................................... 13!
8! MYSQL WORKBENCH: TABLES AND DATA MIGRATION .............................................. 15!
9! CONCLUSION ...................................................................................................................... 16!
10! ADDITIONAL RESOURCES .............................................................................................. 16!
Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
3
1 Introduction
Data is the lifeblood of all successful businesses, no matter the size of the company or the industry they
serve. A company’s data takes many forms: it can be new transactional data in the form of incoming
orders from a Web site, business intelligence data that’s gleaned from customer data, which helps a
company’s management staff make smart strategic decisions, historical information that’s needed for
compliance officers, or metadata that describes the various data elements that make up data-driven
systems and how each one of those elements is used. Should a company mishandle or lose key data, it
is oftentimes a devastating experience and one that can end in great financial costs as well as loss of
reputation.
This being the case, modern and successful companies are leaving nothing to chance when it comes to
the definition, design, and implementation of their data. This equates to a professional and process-driven
approach to the creation and management of data that will flow through its business systems – a process
that is led by data management professionals who utilize a model-driven approach to data and employ
the right tools in the process to ensure that the capture and administration of data is properly carried out.
This paper looks at the various types of data that businesses need to manage, examines the reasons why
a model-driven approach to data management is necessary, and outlines the benefits such an approach
provides. It also highlights how the data modeling module within MySQL Workbench can be an
indispensable aid in the hands of experienced data modelers, developers, and DBAs who are tasked with
managing the complex data management infrastructure of a dynamic and growing business.
2 Types of Data to Manage
Although there are myriads of different ways to classify and categorize data, most forms of data that
modern companies deal with fall into seven groups:
1. Operational data – normally transactional processing data that exists in the form of new/updated
customer orders and other data that supports the products and services that companies sell. This
data is generally found in relational databases that support transactional data flows.
2. Business Intelligence data – exists in the form of current and past operational data that is being
used to understand things like customer purchasing trends, the impact of marketing programs, and
more. This data typically resides in staging areas known as data warehouses or analytic data
stores, and is separated from the operational data to improve system response times for those
systems.
3. Historical data – represents the historical activity of business systems or audit trails of data usage
throughout an organization. It differs from business intelligence data in that it is seldom accessed
and is primarily kept online to meet government or industry compliance regulations.
4. Integration data – used to manage the flow of data from operational systems to analytic or
historical data stores. It most often defines the process of how transactional data is transformed
into business intelligence data.
5. Master data – equates to “reference data”. Reference data does not depend on other data
elements for its identity or meaning, and usually serves as consensus data that is shared
consistently across systems.
6. Metadata – is “data about data” and serves as the definition point of data elements along with
describing how they should be used.
7. Unstructured data – is typically handled in content management systems (although some are
moving this into traditional RDBMS engines), which manages the evolutionary life cycle of digital
information (video files, documents, etc.)
Again, there may be more narrowly-defined classifications of data, but the above represents the bulk of
what today’s modern enterprise tackles in the area of data management.
Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
4
3 Model-Driven Data Management
The approach acknowledged by experienced data professionals as the best method for managing the
capture, definition, and implementation of data throughout an organization is one that is model-driven. That
is, it depends on modeling the use and relationships of data that exist in the categories of data discussed in
the previous section. This applies to both existing systems and ones that are in the process of being
constructed.
Models are the best means for representing the definition of data elements that support the various data
stores found throughout an enterprise. This being the case, it is not surprising that most IT organizations
utilize practices such as entity relationship diagramming (ERD) or other forms of modeling to capture and
preserve their data structures.
4 Model-Driven Data Management Benefits
There are a number of benefits that model-driven data management brings to the table, with the most
tangible being the following:
• Metadata management – ensures data consistency, enforces standards of data elements used
throughout an organization, and assists in identifying and cataloging elements for data governance
• Rapid application delivery – reduces the time it takes to craft and implement a new physical data
design and also the application that makes use of the underlying database
• Change management – helps to manage change between different iterations of data designs
• Packaged application management – removes the ‘black box’ feel of packaged applications by
graphically rendering the heart of any application, which is the database.
• Reporting and communication – greatly simplifies the communication and reporting of new and
modified data designs
• Performance management – helps to more quickly pinpoint design flaws in data designs that
contribute to inefficient response times in actual data-driven systems
Each one of these areas is explored in more detail in the sections that follow.
4.1 Metadata Management
The importance of having well-defined and standardized data element definitions is understood by
companies who have wrestled with the difficult task of tracing and fixing the use of data artifacts that mean
and refer to the same thing, but are defined differently across various applications and systems. Ensuring,
for example, that a data element named CUSTOMER_ID is defined consistently among all systems that
use it (e.g. correct datatype, meaning, etc.) lessens the pain of using it in future applications or in analytic
data stores that are fed from many operational systems.
In addition, government and industry compliance regulations have
created a whole new set of reasons why proper metadata management
is critical. The need to ensure the highest possible data quality (no
invalid data; data matches its purpose and definition 100%), data privacy
(internal only or external), data security (highly sensitive, not sensitive,
etc.), what business area owns the data element (e.g. Finance, etc.), and
what compliance is being ensured (Sarbanes Oxley, etc.) are all needs
that modern enterprises now have.
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