DCIT24 Flashcards
it is concerned with the nature and use of data
data analysis
in practice, go directly from fact finding to implementation dependent data analysis
systems analysts
establishing the nature of data
data analysis
establishing the use of data
functional analysis
the designer creates a written specification in words for the database system to be built
database study
conceptual, logical, and physical design steps in taking specifications to physical implementable designs
database design
it is quite possible that the database is to run on a machine which as yet does not have a database management system running on it
implementation and loading
the database, once implemented, must be tested against the specification supplied by the client
testing and evaluation
the system is actually in real usage by the company
operation
designers rarely get everything perfect first time
maintenance and evolution
often referred to as the three-level model
three-level database model
the design moves from a written specification taken from the real-world requirements to a physically implementable design
three-level database model
three levels of database model
- conceptual design
- data model mapping
- physical design
takes the requirements and produces a high-level data model of the database structure
conceptual design phase
the conceptual schema is converted into database internal structures
physical design phase
process of creating a visual representation of either a whole
information system or parts of it to communicate connections between data points and structures
data modeling
built around business needs
data models
can be modeled at various levels of abstraction
data
types of data models
- conceptual data models
- logical data models
- physical data models
also referred to as domain models
conceptual data models
offer a big-picture view of what the system will contain, how it will be organized, and which business rules are involved
conceptual data models
less abstract and provide greater detail about the concepts and relationships in the domain
logical data models
provide a schema for how the data will be physically stored within a database
physical data models
they’re the least abstract of all
physical data models
data modeling techniques
- hierarchical data modeling
- network data modeling
- relational data modeling
- entity-relationship data modeling
- dimensional data modeling
- object-oriented data modeling
- graph data modeling
organize data in a treelike arrangement of parent and child records
hierarchical data modeling
similar hierarchical method is also used today
XML or extensible markup language
a popular data modeling option in mainframe databases that isn’t used as much now
network data modeling
allowing child records to be connected to multiple parent records
network data modeling
a more flexible alternative to hierarchical and network ones
relational data modeling
visually map entities, their attributes and the relationships between different entities
entity-relationship data modeling
primarily used in data warehouses and data marts that support business intelligence applications
dimensional data modeling
similar to the ER method, but it abstracts entities into objects
object-oriented data modeling
a more modern offshoot of network and hierarchical models
graph data modeling
steps of data modeling
- define an entity
- define key properties for each entity
- identify relationships between entities
- mapping properties to entities
- reduce redundance in performance requirements
- complete and validate the data model
benefits of data modeling
- internal agreement on data definitions and standards
- increased involvement in data management by business users
- more efficient database design at a lower cost
- better use of available data assets
the constraints, policies, and logic that govern how your data behaves and relates to each other
business rules
business rules
- identify the sources
- define the scope
- express the rules
- implement the rules
a collection of tasks or processes that enhance the designing, development, implementation, and maintenance of enterprise data management system
database design
it is not an important discussion that has to be taken forward in this article because we are focused on the database design
life cycle
this stage is concerned with planning the entire DDLC (Database Development Life Cycle)
planning
this stage covers the boundaries and scopes of the proper database after planning
system definition
concerned with the practices and implementations of the logical model
physical model
concerned with developing a model based on the proposed requirements
logical model
used to import and convert data from the old to the new system
data conversion and loading
concerned with error identification in the newly implemented system
testing
a crucial step because it checks the database directly and compares the requirement specifications
testing
process of determining the logical data structures that are required to support information resources within an organization
logical database design
critical to the implementation of a corporate database
logical design
represents the database as a collection of relations
relational model
properties which define a relation
attributes
stored along with its entities
table
it represents records
rows
it represents attributes
column
a single row of a table, which contains a single record
tuple
represents the name of the relation with its
attributes
relation schema
total number of attributes which in the relation
degree
total number of rows present in the table
cardinality
it represents the set of values for a specific attribute
column
a finite set of tuples in the RDBMS system
relation instance
every row has one, two or multiple attributes
relation key
every attribute has some pre-defined value and scope
attribute domain
conditions which must be present for a valid relation
relational integrity constraints
can be violated if an attribute value is not appearing in the corresponding domain or it is not of the appropriate data type
domain constraints
an attribute that can uniquely identify a tuple in a relation
key of the table
based on the concept of Foreign Keys
referential integrity constraints
an important attribute of a relation
foreign key
a type of flowchart that illustrates how “entities” relate to each other within a system
entity relationship (ER) diagram
a definable thing that can have data stored about it
entity
group of definable things
entity type
same as an entity type, but defined at a particular point in time
entity set
an attribute that uniquely defines an entity in an entity set
entity keys
set of attributes (one or more) that together define an entity in an entity set
super key
minimal super key, it has the least possible number of attributes
candidate key
a candidate key chosen by the database designer to uniquely identify the entity set
primary key
how entities act upon each other or are associated with each other
relationship
same entity participates more than once in the relationship
recursive relationship
property or characteristic of an entity
attribute
property or characteristic of a relationship
descriptive attribute
the attribute value is atomic and
can’t be further divided
simple
sub-attributes spring from an attribute
composite
attributed is calculated or otherwise derived from another attribute
derived
more than one attribute value is denoted
multi-value
just one attribute value
single value
cardinality can be shown as look-across or same-side
cardinality views
minimum or maximum numbers that apply to a relationship
cardinality constraints
highest level in a data flow diagram // a tool popular among business analysts
context diagram
first drawn in the middle of the chart // usually a circle shape that represents a conceptual boundary
context bubble
represents all the external components that may interact with the system
system context diagram
