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Usage-Driven Database Design From Logical Data Modeling through Physical Schema Definition

:	George Tillmann
:	Usage-Driven Database Design From Logical Data Modeling through Physical Schema Definition
:	Apress
:	9781484227220
:	1
:	CHF 71.00
:

:	Informatik
:	English

:	379
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Design great databases-from logical data modeling through physical schema definition. You will learn a framework that finally cracks the problem of merging data and process models into a meaningful and unified design that accounts for how data is actually used in production systems.

div>Key to the framework is a method for taking the logical data model that is a static look at the definition of the data, and merging that static look with the process models describing how the data will be used in actual practice once a given system is implemented. The approach solves the disconnect between the static definition of data in the logical data model and the dynamic flow of the data in the logical process models.

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The design framework in this book can be used to create operational databases for transaction processing systems, or for data warehouses in support of decision support systems. The information manager can be a flat file, Oracle Database, IMS, NoSQL, Cassandra, Hadoop, or any other DBMS.

Usage-Driven Database Design emphasizes practical aspects of design, and speaks to what works, what doesn't work, and what to avoid at all costs. Included in the book are lessons learned by the author over his 30+ years in the corporate trenches. Everything in the book is grounded on good theory, yet demonstrates a professional and pragmatic approach to design that can come only from decades of experience.

Presents an end-to-end framework from logical data modeling through physical schema definition.

Inclu es lessons learned, techniques, and tricks that can turn a database disaster into a success.

Applies to all types of database management systems, including NoSQL such as Cassandra and Hadoop, and mainstream SQL databases such as Oracle and SQL Server

< iv>What You'll Learn

C eate logical data models that accurately reflect the real world of the user
Create usage scenarios reflecting how applications will use a new database
Merge static data models with dynamic process models to create resilient yet flexible database designs
Support application requirements by creating responsive database schemas in any database architecture
Cope with big data and unstructured data for transaction processing and decision support systems
Recognize when relational approaches won't work, and when to turn toward NoSQL solutions such as Cassandra or Hadoop

Who This Book Is For
Syste developers, including business analysts, database designers, database administrators, and application designers and developers who must design or interact with database systems

George Tillmann is a retired Booz, Allen Hamilton partner; a former programmer, analyst, management consultant; and CIO who managed Booz Allen's global IT organization. He brings more than 30 years experience as a database administrator, database consultant, and database product designer. He has written two books, was a Computerworld columnist, and has articles published in CIO, Infoworld, Techworld, Data Base, The Standard, Database Programming& Design and is a former member of the ANSI/X3/SPARC Data Base Management Systems Study Group.

	Contents at a Glance	5
	Contents	7
	About the Author	18
	Preface	19
	Part I: Introduction	23
	Chapter 1: Introduction to Usage-Driven Database Design	24
	Database Design Principle 1: Separation Principle	26
	Database Design Principle 2: Distinction Principle	27
	The Difference Between Separation and Distinction	29
	Database Design Principle 3: Convergence Principle	29
	The Separation, Distinction, and Convergence Principles	30
	Database Design Principle 4: Minimal Regression Principle	30
	Usage-Driven Database Design	30
	Logical Data Modeling	31
	Physical Schema Definition	32
	The Terminology Trap	32
	Notes	33
	Part II: Logical Data Modeling	34
	Chapter 2: The E-R Approach	35
	A Little Data Modeling History	37
	Some Important Definitions	38
	Logical Data Modeling Objects	39
	Entities	39
	Type-Instance Distinction	40
	Relationships	40
	Attributes	41
	Notes	42
	Chapter 3: More About the E-R Approach	43
	More About Relationships	43
	Membership Class	43
	Cardinality	44
	Modality	45
	Degree	47
	Binary Relationship	47
	N-ary Relationships	48
	Unary or Recursive Relationships	48
	Relationship Constraints	49
	Inclusion	49
	Exclusion	50
	Conjunction	50
	Simple Conjunction	50
	Conditional Conjunction	51
	Recursive Modality Constraints	52
	More About Entities	55
	Attributive Entity	55
	Associative Entities	56
	Supertype and Subtype Entities (Generalization and Specialization)	56
	More About Attributes	58
	Attribute Domain	58
	Attribute Source: Primitive and Derived	59
	Attribute Descriptor and Unique Identifier	59
	Compound or Concatenated Unique Identifiers	60
	Attribute Complexity: Simple and Group	60
	Attribute Valuation: Single Value and Multivalue	61
	Attribute Complexity and Valuation	61
	Chapter 4: Building the Logical Data Model	64
	The Interview Process	65
	Gather Information and Review	66
	1. Identify the Users Who Are Authorities or Experts on the Subject	66
	2. Meet and Interview the Experts and Identify the Subject (Application) Entities	66
	Preparation	66
	The First Interview	67
	3. Identify Relationships Between the Entities	67
	4. Identify the Properties or Attributes of the Entities and Relationships	67
	Analyze Information	67
	Construct Model	68
	Repeat as Necessary	68
	Making Sense of the Interview	68
	Modeling Rules	70
	Verifying What You Have Heard	72
	Immediate Interview Feedback	72
	Formal Walk-Throughs	72
	Increasing E-R Diagram Comprehension	74
	Subject Areas	74
	Entity Fragments	75
	Neighborhood Diagrams	76
	Relationship Bridges and Stubs	77
	Some Model Building Best Practices	78
	Getting Started	78
	Don’t Lose Control of the Project to Users	79
	Don’t Lose Control of the Project to Technical Staff	79
	Don’t Become Dependent on Tools or Techniques	80
	Don’t Get Bogged Down in Endless Analysis	80
	The Players…and the Rules of Engagement	81
	Deliverables	82
	Examples of Deliverables	83
	Sample Data Dictionary, Data Object Definitions	84
	Notes	86
	Chapter 5: LDM Best Practices	87
	Abbreviations	88
	Almost Unique Identifiers	89
	Clarity	90
	Compound Unique Identifiers	90