data models Flashcards
focuses on how the database structure will be used to store and manage end-user data
database design
the first step in designing a database
data modeling
the process of creating a specific data model for a determined problem domain
data modeling
a relatively simple representation, usually graphical, of more real-world data structures.
data model
an abstraction of a more complex real-world object or event
model
building blocks for data model:
- entity
- attribute
- relationship
a person, place, thing or event about which data will be collected and stored
entity
a characteristic of an entity
attribute
describes an association among entities
relationship
three types of relationships:
- one-to-one (1:1) relationship
- one-to-many (1:M) relationship
- many-to-many (M:M) relationship
this model’s basic logical structure is represented by an upside-down tree.
it contains levels or segments
segments is the equivalent of a file system’s record type
developed in the 1960’s to manage large amounts of data for complex manufacturing projects
hierarchical model
data model that was created to represent complex data relationships to improve database performance and to impose database standard
network model
it is the conceptual organization of the entire database
schema
it defines the portion of the database by the application programs that actually produce the desired info from the data in the database
subschema
it defines the environment in which data can be managed
data manipulation language (DML)
it allows the database administrator to define the schema components
data definition language (DDL)
data model that was introduced in 1970 by E. F. Codd of IBM, represented a major breakthrough for both users and designers
relation - foundation of mathematical concept
relational model
data model that was introduced in 1976 by peter chen
entity relationship model
a metalanguage used to represent and manipulate data elements.
extensible markup language (XML)
it refers to a movement to find new and better ways to manage large amounts of web and sensor-generated data and derive business insight from it
big data
the basic characteristics of big data databases that was described by Douglas Laney, a data analyst from the Gartner Group:
volume, velocity, variety
it refers to the amounts of data being stored
volume
it refers not only to the speed with which data grows but also to the need to process this data quickly in order to generate information and insight
velocity
it refers to the fact that the data being collected comes in multiple different data formats
variety
it is a large-scale distributed database system that store structured and unstructured data in efficient ways
noSQL
general characteristics of noSQL databases:
- they are not based on the relational model and SQL
- they support distributed database architectures
- they provide high-scalability, high availability, and fault tolerance
- they support very large amounts of sparse data
- they are geared toward performance rather than transaction consistency
advantages of noSQL:
- noSQL supports distributed database architecture
- noSQL supports very large amounts of sparse data
- noSQL provides high scalability, high availability, and fault tolerance
- most noSQL databases are geared toward performance rather than transactions consistency
in 1970’s, they defined a framework for data modeling based on degrees of data abstraction.
american national standards institute (ANSI) standards planning and requirements committee (SPARC)
the resulting ANSI/SPARC architecture defines three levels of data abstraction:
external, conceptual, and internal
it is the end user’s view of the data environment
external model
it represents a global view of the entire database by the entire organization
conceptual model
it is the representation of the database as “seen” by the DBMS
internal model
depicts a specific representation of an internal model, using the database constructs supported by the chosen database
internal schema
operates at the lowest level of abstraction, describing the way data is saved on storage media such as magnetic, solid stage, or optical media.
physical model
