foundations of database systems class introduction g. green 1

Foundations of Database Systems

Class Introduction

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Agenda

• Introductions• Seating Chart• Course Overview• Syllabus• Case• Database Development Overview

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Foundations of Database Systems

ObjectivesUnderstand data-related activities of SDLCImplement data modeling, database design, and database

implementation techniques CASE (Visio) Database (SQL Server)

Course ContentsLectures, Examples, In-Class ExercisesIndividual Assignments (3)Team Project* (3 parts)Quizzes (3)Exams (2)

*Can request teammates; see syllabus for Team Preferences deadline

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Research

• Service Learning & Kolb’s Learning Cycle• International and US

• Periodic Assessments• Some NOT graded; others are

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Learning

Participate :› Prepare --read & reread book, notes-- for each class › Attend, listen, be attentive, engaged› Ask and answer questions, & add to discussion› Do each assignment completely & in a timely and professional

manner

Take PLENTY of notes in class:› Do NOT just rely on powerpoint

Explore :› Go beyond classroom material

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Class Resources

Syllabus/Schedule, Grades, Attendance: http://canvas.baylor.edu Schedule also contains links to all lecture slides, study guides,

assignments and project write-ups

Other Resources: http://blogs.baylor.edu/gina_green/mis-4340-resources/ NOTE: the syllabus/schedule on this website will NOT contain the

links described above

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Syllabus…

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Introduction to Databases

Chapter 1

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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

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Evolution of Database Technologies

1960’s 1970’s 1980’s 1990’s 2000+

Federated

MDDB

XML

NoSQL

…….

Traditional Files

Hierarchical

Network

Relational

Object

Object-Relational

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Figure 1-3 Old file processing systems: Example

Duplicate Data

Traditional File Processing Environment

Disadvantages:› Program-data dependence = “structural” & “data”› Limited data sharing = “islands of automation”› Duplication of data = “redundancy”› Lengthy development times› Excessive program maintenance

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The Database Environment

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Advantages of Databases

Program-data independenceImproved data sharingMinimal data redundancyImproved data accessibility/responsiveness Improved data consistencyFaster application developmentEnforcement of standardsImproved data qualityReduced program maintenance

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Data and Database Administration

Chapter 11

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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

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Data People Involved in SDLCData Administrators

Data(base) Analysts/Designers requirements elicitation, designBusiness (Intelligence) Analyst BI requirements, designData Architects strategy, governanceData Stewards quality, metadata, MDMBusiness Analytics Engineer data analytics, statistics, miningData Mining Engineer; Big Data “big data” specialists

Engineer; Data Scientist …

Database Administrators(System) DBAs implementation/maintenanceApplication DBAs

Procedural DBAs stored code e-DBAs web-enabled DBMSs

Data Warehouse Administrators ETL, DW implementation

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Growing Skillset• Relational database design, implementation• Database programming• ETL (extract, translate, load)• Data warehousing design (star schema) and implementation

(MDDB)• Data analysis, reporting, and mining techniques• Cloud database implementations• Statistical modeling with tools such as R, SAS, or SPSS• Data visualization tools• Technologies for structured and unstructured data• Hadoop (Hadoop is 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

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Data Quality and Integration

Chapter 10

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Metadata Management• System Catalog• Part of DBMS• "Active" dictionary

• Data Dictionary • Typically "passive"• Extension of catalog metadata

• Information Repository (e.g., IRDS)• Standards for data dictionaries• Integrates dictionaries

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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

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Database Development Process

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Systems Development Life Cycle

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Planning

Analysis

Design

Implementation

Enterprise Modeling*

DB Scope, Requirements(Conceptual Data Model)

DB Design(Logical DB Design)

DB Design (Physical DB Design)

DB Implementation(Load, Test, Eval, Op)

DB Maintenance*

DB Activities in SDLCSDLC for this class

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

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Source: http://www.tdan.com/view-articles/5205

Conceptual Data Modeling

Determine user data requirements

Determine business rules

Build conceptual data model› outcome is an Entity-Relationship Diagram

(conceptual schema)

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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

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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

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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

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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…

• Backup• Full • Incremental• Differential• Business Continuity• Data Replication ("fallback")

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Data and Database Administration

Chapter 11

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Cloud Computing

• Business Model• Computing resources on demand• Need-based architectures• Internet-based delivery• Pay as you go

• History (VERY high-level and approximate)

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Time-sharing

Virtual Machines

Utility Computing

WWW

Personal Computers

Grid Computing

Cloud Computing

50's 60's 70's 80's 90's 2000's

Cloud Computing Services

• Impacts to Data(base) Administration• See textbook page 469

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Summary• Evolution of Data Management• Disadvantages of file processing

• Database Concepts• Components of a DBMS Environment• Database Advantages

• Database Development:• Overall SDLC• Database Activities in the SDLC

• Data Models/Schemas• What they represent

• People Involved in SDLC (esp. DB)• Traditional job divisions and responsibilities• Newer job titles

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