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Key Terms

  • CRUD: Create, Read, Update, Delete
  • ACID: Atomicity, Consistency, Isolation, Durability
  • Normalization
  • CAP Theorem
  • Schema

http://en.wikipedia.org/wiki/CAP_theorem http://en.wikipedia.org/wiki/ACID http://en.wikipedia.org/wiki/Create,_read,_update_and_delete

Relational vs Flat

https://kb.iu.edu/d/ahrp

Relational

  • more flexible
  • more compact if data relations are possible
  • converting a relational DB to flat-file, is much easier than the reverse

Flat

  • no special software required
  • performance gets worse and worse as it grows
  • data can become duplicated and inconsistent

Structured Query Language

http://en.wikipedia.org/wiki/SQL

  • purpose specific programming language for managing data
  • most widely used language for database manipulation
  • while standardized, code isn't entirely portable due to vendor specific features
  • not all standardized syntax is supported by all implementations

NoSQL

http://en.wikipedia.org/wiki/NoSQL

  • may implement some subset of SQL
  • generally associated with big data, and high scalability (large size or extremely high IO)
  • each implementation is best for certain tasks (not a general purpose database)
  • Examples
    • Apache Cassandra
    • mongoDB
  • data storage model varies
  • most modern implementations offer some type of distributed model, e.g. multiple storage nodes containing parts of the database
  • MapReduce is one method/purpose for using some NoSQL systems

Relational

  • offer SQL, along with proprietary extensions
  • general purpose, fits most use cases
  • all popular database engines support ODBC allowing for wide application support
  • Examples
    • Storing sample data, you could have a table containing test sites, and tables for each kind of test done. Then relate each test with a sample site.
    • Storing interview data. Say you plan to interview a person several times during a study. You could have a table containing interviewees, and a table containing interviews taken.
  • most relation databases support multiple users with concurrent access
  • SQLite, MySQL, and Microsoft SQL Server are some example engines
  • most implementations include constraint checks

Flat

  • flexible structure, CSV, TSV, and Fixed Width are common. Even an Excel spreadsheet could be a flat database
  • no database engine required
  • some ODBC implementations offer a flat file driver, enabling limited SQL support
  • best used for data where no relation is needed, or where every row is related to one thing.
    • For example, storing the temperature from a single temp sensor.
  • no builtin concurrent access