1. What is database?

Motivation

• Database
  • Organized collection of inter-related data that models some aspect of the real-world.
  • Databases are one of the core components of most computer applications
• Database Examples
  • Universities: Registration, grades
  • Financial market
    • credit card transactions
    • Sales and purchases information of stocks and bonds
    • Real-time market data
  • Enterprise information
    • Sales: customer products, purchases
    • Accounting: payments, receipts, assets
    • Human resources: employee profile, salaries, taxes

File System

In the early data, database applications were built directly on top of file systems:

• Data redundancy and inconsistency
  • Data is stored in multiple file formats and locations
  • → Resulting induplication of information
• Difficulty in accessing data
  • Need to write a new program to carry out each new task
• Data isolation
  • Meaning: a property that determines when and how changes made by one operation become visible to others
  • Cannot be controlled with files
• Integrity problems
  • Integrity constraints (ex. account balance ≥ 0) become "buried: in program code, rather than being stated explicitly
• Concurrency problems
  • Uncontrolled concurrent accesses can lead to inconsistencies
• Security problems
  • Hard to provide a fine-grained user access control

→ Database systems offer solutions to all the above problems

Brief history

• ~early 1960s:
  • Data processing using magnetic tapes for storage
    • Tapes provided only sequential access
  • Punched cards for input
  • System 360(IBM), Random Acess, Sequential Access
• Late 1960s~ and 1970s
  • Hard disks allowed direct access to data
  • Network and hierarchical data models in use
  • Ted Codd defined the relational data model
    • The work won the ACM Turing Awards (1981)
    • IBM Research began System R prototype
    • UC Berkeley (Michael Stonebraker) began Ingres prototype
    • Oracle released first commercial relational database
• 1980s
  • Research relational prototypes evlove into commercial systems
    • SQL becomes industrial standard
  • Parallel and distributed database systems
    • Wisconsin, IBM, Teradata
  • Object-oriented database system
• 1990s
  • Large decision support and data-mining application
  • Large multi-terabyte data warehouse
  • Emergence of Web commerce
• 2000s
  • Big data storage systems:Google BigTable, Yahoo PNuts, Amazon, NoAQL systems
  • Big data analysis: beyond SQL
    • Map reduce