1. Data at the Core of the Enterprise

2. Objectives  Define of database systems  Introduce data modeling and SQL  Discuss emerging requirements of database systems

3. DATA INFORMATION ?

4. Attributes of data  Sharable  Moveable  Secure  Accurate  Timely  Relevant

5. Data hierarchy  Bits  Characters  Fields (columns)  Records (rows)  Files (table)  Database

6. Why build a database?  Handle large amounts of data  Satisfy multiple users  Make information retrieval faster  Make data input faster  Provide greater accuracy

7. Database versus Database Management System (DBMS)  Database is a self-describing collection of integrated files  A DBMS is a complex computer program that acts as a data librarian, supervising the transfer of data between the end user and the database

8. Advantages of DBMS  More info from the same data  Reduction of data duplication  Improved data integrity  Programs are independent of the data format  Sharing of data resources

9. …and disadvantages  Added expense  More hardware may be needed  If it crashes….  Sophisticated design and programming required  Additional training  Security is critical

10. Relational model  Relation? Attribute? Tuple?  Keys  Primary and foreign  Referential integrity  Relational algebra

11. Relational DB rules  Every row must have exactly the same number of columns (fields or attributes)  Each row can have only one value stored in each column (fields or attributes)  A column must contain the same kind of value in every row of that column  No two rows can be exactly the same  The order of the rows or of the columns can’t be used to provide information

12. Terminology Data Processing Informal Relational DB Formal Relational DB File Table Relation RecordRowTuple FieldColumnAttribute

14. Data modeling  Purpose: control and visualization  Process: gathering requirements  Results: forms and diagrams

15. Normalization  Purpose:  Avoid anomalies  Reduce redundancy  Process:  Successive application of rules  Bottom-up (data drives process)  Move from first through fifth normal form  Does it make more or less tables?

17. Entity relationship modeling  List the entities or objects in the environment  People, things, transactions  Describe the relationship between them  A single row in table A can be related to how many rows in table B (one or many)  A single row in table B can be related to how many rows in table A (one or many)

18. ERD questions  What are the subjects/objects of the business? data entities  What unique characteristic(s) distinguishes each object from others of same type? primary key  What characteristics describe each object? attributes  How do you use this data? controls & meaning

19. ERD questions  Over what period of time are you interested in this data? cardinality & time dimensions  Are all instances of each object the same? supertypes, subtypes, aggregations  What events occur that imply associations between objects? relationships (cardinality & degree)  Is each activity or event always handled the same way or are there special circumstances? integrity rules, cardinality, time

21. SQL  Definition (DDL)  CREATE, ALTER, DROP  Manipulation (DML)  SELECT, INSERT, UPDATE, DELETE  What’s the most used sql command?

22. General format of SELECT SELECT [DISTINCT] item(s) FROM table(s) [WHERE condition] [GROUP BY columns] [HAVING condition] [ORDER BY row(s)]

23. Current RDBMS Emerging data requirements Complex Simple UnstructuredStructured Audio Streams Images Video Streams Text Graphics Audio Spatial Data Time Series HTML/ SGML Video

24. Summary  Defined of database systems  Introduced data modeling and SQL  Discussed emerging requirements of database systems