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Introduction to Databases and Database Development. Chapters 1, 9*, 10 *, and 11*. Topics. Chapter 1 The Database Environment Database Development Process Chapter 9 (Pages 409 – 410) Big Data Chapter 10 (Pages 444 – 445; 446 - 447) Master Data Management Data Federation

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topics
Topics
  • Chapter 1
    • The Database Environment
    • Database Development Process
  • Chapter 9 (Pages 409 – 410)
    • Big Data
  • Chapter 10 (Pages 444 – 445; 446 - 447)
    • Master Data Management
    • Data Federation
  • Chapter 11 (Pages 464 – 472, 486, 499 – 506)
    • Database Personnel
    • Metadata Management (e.g., Data Dictionaries)
    • Backup Facilities
    • Overview of Tuning the Database for Performance
slide4

1960’s

1970’s

1980’s

1990’s

2000+

Federated

MDDB

XML

NoSQL

…….

Hierarchical

Object

Traditional Files

Relational

Network

Object-Relational

Evolution of Database Technologies

slide5

Duplicate Data

Figure 1-3 Old file processing systems: Example

traditional file processing environment
Traditional File Processing Environment
  • Dates back to before we had databases
  • Still in use today, including
    • Backup of database systems
    • Import/export data between systems
    • Some statistical analyses
  • Disadvantages:
    • Program-data dependence = “structural” & “data”
    • Limited data sharing = “islands of automation”
    • Duplication of data = “redundancy”
    • Lengthy development times
    • Excessive program maintenance
database environment cont
Database Environment, cont…
  • Hardware
  • Software
    • OS
    • Applications
      • User interface
      • Application programs
      • CASE tools and utilities
    • Database Management System
  • People
    • End Users
    • System Developers
    • Data & Database Administrators
  • Database
    • User data
    • Metadata (Repository)
  • Procedures
    • Backup/Recovery
    • Retention
    • Ownership/Location…
advantages of databases
Advantages of Databases
  • Program-data independence
  • Improved data sharing
  • Minimal data redundancy
  • Improved data accessibility/responsiveness
  • Improved data consistency
  • Faster application development
  • Enforcement of standards
  • Improved data quality
  • Reduced program maintenance
slide11
SDLC

SDLC for this class

DB Activities in SDLC

Planning

Enterprise Modeling*

DB Scope, Requirements

(Conceptual Data Model)

Analysis

DB Design

(Logical DB Design)

Design

DB Design

(Physical DB Design)

DB Implementation

(Load, Test, Eval, Op)

Implementation

DB Maintenance*

enterprise data modeling
Enterprise Data Modeling
  • Determine organizational data requirements
  • Build enterprise data model
    • outcome is a very high-level Entity-Relationship Diagram
    • see :
      • http://da.ks.gov/kito/ITPlans/data_maps06.ppt
      • http://www.tdan.com/view-articles/5205
conceptual data modeling
Conceptual Data Modeling
  • Determine user data requirements
  • Determine business rules
  • Build conceptual data model
    • outcome is an Entity-Relationship Diagram (conceptual schema)
logical database design
Logical Database Design
  • Select database model
    • e.g., the Relational Model
  • Transform conceptual (ERD) into logical (relational) data model
  • Normalize data structures
    • Outcome is normalized, relational tables
physical database design
Physical Database Design
  • Select database product (e.g., SQL Server)
  • Select storage device(s)
  • Design fields, records, files (physical schema)
    • outcomes are detailed, physical definitions for:
      • fields (data dictionary)
      • records (space requirements for physical structures)*
      • files (access methods)

*Will not do in this class

data models
Data Models
  • What is a model?
  • American National Standards Institute / Standards Planning and Requirements Committee (ANSI/SPARC) classifications:
    • Conceptual
    • External
    • Internal (Logical)
    • Physical*
  • Database design / development involves creating these data models
slide18

Figure 1-12 Three-schema architecture

Different people have different views of the database…these are the external schema

The internal schema is the underlying design and implementation

database implementation
Database Implementation
  • Create database file/table structures
  • Create views (external schema)
  • Establish access rights
  • Load test data
  • Write/test programs that process data
  • Install database (with production data) into production operations
    • outcomes are secured database tables loaded with data
database maintenance
Database Maintenance
  • Maintain database structures
    • Storage/space management
  • Performance, tuning
    • I/O Contention
    • CPU Usage
    • Application Tuning
  • Data availability
  • DBMS upgrades, "fixes"
  • Backup, recovery …….
database maintenance cont
Database Maintenance, cont…
  • Backup
    • Full
    • Incremental
    • Differential
  • Business Continuity
    • Data Replication ("fallback")
traditional administration definitions
Traditional Administration Definitions
  • Data Administration: A high-level function that is responsible for the overall management of data resources in an organization, including maintaining corporate-wide definitions and standards
  • Database Administration: A technical function that is responsible for physical database design and for dealing with technical issues such as security enforcement, database performance, and backup and recovery
data people involved in sdlc
Data People Involved in SDLC
  • Data Administrators
    • Data Architects
    • Data Stewards
    • Data(base) Analysts/Designers
    • Business (Intelligence) Analyst
    • Data Mining Engineer; Big Data Engineer; Data Scientist; Business Analytics Engineer; …
  • (System/Traditional) DBAs
    • Application DBAs
      • Procedural DBAs
      • e-DBAs
    • Data Warehouse Administrators
metadata management
Metadata Management
  • System Catalog
    • Part of DBMS
  • Data Dictionary
    • Typically passive
    • Extension of catalog metadata
  • Information Repository
  • Master Data Management

(see ch. 10 pgs 444 – 445)

master data management
Master Data Management
  • "Ensuring the currency, meaning, and quality of reference data within and across various subject areas" (pg 444)
  • Identify
    • Common Data Subjects
    • Common Data Elements
    • Sources of "the truth"
  • Cleanse
  • Update applications to reference Master Data repository
  • Ensures consistency of key data (not ALL data) throughout organization
cloud computing
Cloud Computing
  • Business Model
    • Computing resources on demand
    • Need-based architectures
    • Internet-based delivery
    • Pay as you go
  • History (VERY high-level and approximate)

Cloud Computing

Time-sharing

Utility Computing

Personal Computers

Virtual Machines

WWW

Grid Computing

50's

60's

70's

80's

90's

2000's

cloud computing services
Cloud Computing Services
  • Impacts to Data(base) Administration
    • See textbook page 469
big data skills
"Big Data" Skills
  • ETL (extract, translate, load)
  • Data warehousing (MDDB)
  • Data mining techniques
  • Statistical modeling with tools such as R, SAS, or SPSS
  • Data visualization tools
  • Technologies for structured and unstructured data
    • Hadoop (an Apache project to provide an open-source implementation of frameworks for reliable, scalable, distributed computing and data storage.)
    • NoSQL
    • "NewSQL"

See Big Data University for (mostly) free self-study training

Source: http://blog.simplyhired.com/2012/05/carving-out-a-career-in-big-data.html#ixzz243BiPXjF

summary
Summary
  • Evolution of Data Management
    • Description & disadvantages of file processing
    • Description of data management technologies
  • Database Concepts
    • Components of a DBMS Environment
    • Database Advantages
  • Database Development:
    • Overall SDLC
    • Database Activities in the SDLC
  • Data Models/Schemas
    • Types
    • What they represent
  • People Involved in SDLC (esp. DB)
    • Major job divisions and responsibilities
    • Newer job titles
next time
Next Time…
  • Conceptual Data Modeling
    • Chapter 2: Modeling Data in the Organization
    • Chapter 3: Enhanced E-R Model
  • Assignment 1: DB Development ***DUE 1/23***