database lifecycle
DESCRIPTION
database development lifecyle in powerpoint.TRANSCRIPT
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Database Design
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In this lesson, you will learn:
• That successful database design must reflect the information system of which the database is a part
• That successful information systems are subject to frequent evaluation and revision within a framework known as the Systems Development Life Cycle (SDLC)
• That, within the information system, the most successful databases are subject to frequent evaluation and revision within a framework known as the Database Life Cycle (DBLC)
• How to conduct evaluation and revision within the SDLC and DBLC frameworks
• What database design strategies exist: top-down vs. bottom-up design and centralized vs. decentralized design
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• Data – Raw facts stored in databases– Need additional processing to become useful
• Information– Required by decision maker – Data processed and presented in a meaningful
form– Transformation
Changing Data into Information
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• Database – Carefully designed and constructed repository of facts – Part of an information system
• Information System– Provides data collection, storage, and retrieval– Facilitates data transformation– Components include:
• People• Hardware• Software
–Database(s)–Application programs–Procedures
The Information System
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• System Analysis– Establishes need and extent of an information system
• Systems development – Process of creating information system
• Database development– Process of database design and implementation– Creation of database models– Implementation
• Creating storage structure• Loading data into database• Providing for data management
The Information System (Con’t.)
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Systems Development Life Cycle
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Database Lifecycle (DBLC)
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Phase 1: Database Initial Study
• Purposes– Analyze company situation
• Operating environment• Organizational structure
– Define problems and constraints– Define objectives– Define scope and boundaries
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Initial Study Activities
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Phase 2: Database Design
• Most Critical DBLC phase• Makes sure final product meets requirements• Focus on data requirements• Sub phases
– Create conceptual design– DBMS software selection– Create logical design– Create physical design
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Two Views of Data
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I. Conceptual Design
• Data modeling creates abstract data structure to represent real-world items
• High level of abstraction• Four steps
– Data analysis and requirements– Entity relationship modeling and normalization– Data model verification– Distributed database design
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Data analysis and Requirements
• Focus on:– Information needs– Information users– Information sources– Information constitution
• Data sources– Developing and gathering end-user data views– Direct observation of current system– Interfacing with systems design group
• Business rules
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Entity Relationship Modeling and Normalization
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E-R Modeling is Iterative
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Concept Design: Tools and Sources
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Data Model Verification
• E-R model is verified against proposed system processes– End user views and required transactions– Access paths, security, concurrency control– Business-imposed data requirements and constraints
• Reveals additional entity and attribute details• Define major components as modules
– Cohesivity– Coupling
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E-R Model Verification Process
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Iterative Process of Verification
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Distributed Database Design
• Design portions in different physical locations
• Development of data distribution and allocation strategies
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II. DBMS Software Selection
• DBMS software selection is critical • Advantages and disadvantages need study• Factors affecting purchasing decision
– Cost– DBMS features and tools– Underlying model– Portability– DBMS hardware requirements
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III. Logical Design
• Translates conceptual design into internal model • Maps objects in model to specific DBMS
constructs• Design components
– Tables– Indexes – Views– Transactions– Access authorities– Others
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IV. Physical Design
• Selection of data storage and access characteristics– Very technical– More important in older hierarchical and network
models• Becomes more complex for distributed systems• Designers favor software that hides physical
details
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Physical Organization
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Phase 3: Implementation and Loading
• Creation of special storage-related constructs to house end-user tables• Data loaded into tables• Other issues
– Performance– Security– Backup and recovery– Integrity– Company standards– Concurrency controls
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Phase 4: Testing and Evaluation
• Database is tested and fine-tuned for performance, integrity, concurrent access, and security constraints
• Done in parallel with application programming• Actions taken if tests fail
– Fine-tuning based on reference manuals– Modification of physical design– Modification of logical design– Upgrade or change DBMS software or hardware
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Phase 5: Operation
• Database considered operational• Starts process of system evaluation• Unforeseen problems may surface• Demand for change is constant
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Phase 6: Maintenance and Evaluation
• Preventative maintenance• Corrective maintenance • Adaptive maintenance• Assignment of access permissions • Generation of database access statistics to
monitor performance• Periodic security audits based on system-
generated statistics• Periodic system usage-summaries
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DB Design Strategy Notes
• Top-down– 1) Identify data sets– 2) Define data elements
• Bottom-up– 1) Identify data elements– 2) Group them into data sets
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Top-Down vs. Bottom-Up
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Centralized vs. Decentralized Design
• Centralized design– Typical of simple databases– Conducted by single person or small team
• Decentralized design– Larger numbers of entities and complex
relations– Spread across multiple sites– Developed by teams
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Decentralized Design
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People Who Deal With Databases• DB Implementers:
– build DBMS software e.g. at IBM, Oracle
• End Users: – store and use data in a DBMS
• Application Programmers: – develop packages facilitating data access for end
users
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People Who Deal With Databases• DB Administrators (DBA):
– Interact with system users to understand what data is to be stored & how it will be used
– Ensure unauthorized data access not permitted– Ensure if system fails, users can continue accessing
uncorrupted data– Restore data to consistent state
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End