A database is an organized collection of
data. It is a collection of schemas, tables, queries,
reports, views,
and other objects. Database designers typically organize the data to model
aspects of reality in a way that supports processes
requiring information, such as (for example) modelling the availability of
rooms in hotels in a way that supports finding a hotel with vacancies.
data. It is a collection of schemas, tables, queries,
reports, views,
and other objects. Database designers typically organize the data to model
aspects of reality in a way that supports processes
requiring information, such as (for example) modelling the availability of
rooms in hotels in a way that supports finding a hotel with vacancies.
A database-management system (DBMS) is a computer-software application
that interacts with end-users,
other applications, and the database itself to capture and analyze data. A
general-purpose DBMS allows the definition, creation, querying, update, and
administration of databases.
that interacts with end-users,
other applications, and the database itself to capture and analyze data. A
general-purpose DBMS allows the definition, creation, querying, update, and
administration of databases.
A
database is not generally portable
across different DBMSs, but different DBMSs can interoperate by using standards
such as SQL and ODBC or JDBC to allow a single application to work with more
than one DBMS. Computer scientists may classify database-management systems
according to the database models
that they support; the most popular database systems since the 1980s have all
supported the relational model –
generally associated with the SQL language. Sometimes a DBMS is loosely referred to
as a “database”.
database is not generally portable
across different DBMSs, but different DBMSs can interoperate by using standards
such as SQL and ODBC or JDBC to allow a single application to work with more
than one DBMS. Computer scientists may classify database-management systems
according to the database models
that they support; the most popular database systems since the 1980s have all
supported the relational model –
generally associated with the SQL language. Sometimes a DBMS is loosely referred to
as a “database”.
Formally,
a “database” refers to a set of related data and the way it is
organized. Access to this data is usually provided by a “database
management system” (DBMS) consisting of an integrated set of computer
software that allows users
to interact with one or more databases and provides access to all of the data
contained in the database (although restrictions may exist that limit access to
particular data). The DBMS provides various functions that allow entry, storage
and retrieval of large quantities of information and provides ways to manage
how that information is organized. Because of the close relationship between
them, the term “database” is often used casually to refer to both a
database and the DBMS used to manipulate it.
a “database” refers to a set of related data and the way it is
organized. Access to this data is usually provided by a “database
management system” (DBMS) consisting of an integrated set of computer
software that allows users
to interact with one or more databases and provides access to all of the data
contained in the database (although restrictions may exist that limit access to
particular data). The DBMS provides various functions that allow entry, storage
and retrieval of large quantities of information and provides ways to manage
how that information is organized. Because of the close relationship between
them, the term “database” is often used casually to refer to both a
database and the DBMS used to manipulate it.
Outside
the world of professional information
technology, the term database is often used to refer
to any collection of related data (such as a spreadsheet
or a card index). This article is concerned only with databases where the size
and usage requirements necessitate use of a database management system.
the world of professional information
technology, the term database is often used to refer
to any collection of related data (such as a spreadsheet
or a card index). This article is concerned only with databases where the size
and usage requirements necessitate use of a database management system.
Existing
DBMSs provide various functions that allow management of a database and its
data which can be classified into four main functional groups:
DBMSs provide various functions that allow management of a database and its
data which can be classified into four main functional groups:
- Data definition – Creation, modification and removal of
definitions that define the organization of the data. - Update – Insertion, modification, and deletion of
the actual data. - Retrieval – Providing information in a form directly
usable or for further processing by other applications. The retrieved data
may be made available in a form basically the same as it is stored in the
database or in a new form obtained by altering or combining existing data
from the database. - Administration – Registering and monitoring users, enforcing
data security, monitoring performance, maintaining data integrity, dealing
with concurrency control, and recovering information that has been
corrupted by some event such as an unexpected system failure.
Both
a database and its DBMS conform to the principles of a particular database model.
“Database system” refers collectively to the database model, database
management system, and database.
a database and its DBMS conform to the principles of a particular database model.
“Database system” refers collectively to the database model, database
management system, and database.
Physically,
database servers
are dedicated computers that hold the actual databases and run only the DBMS
and related software. Hardware database accelerators, connected to one or more
servers via a high-speed channel, are also used in large volume transaction
processing environments. DBMSs are found at the heart of most database
applications. DBMSs may be built around a custom multitasking kernel
with built-in networking
support, but modern DBMSs typically rely on a standard operating system
to provide these functions.
database servers
are dedicated computers that hold the actual databases and run only the DBMS
and related software. Hardware database accelerators, connected to one or more
servers via a high-speed channel, are also used in large volume transaction
processing environments. DBMSs are found at the heart of most database
applications. DBMSs may be built around a custom multitasking kernel
with built-in networking
support, but modern DBMSs typically rely on a standard operating system
to provide these functions.
Since
DBMSs comprise a significant market,
computer and storage vendors often take into account DBMS requirements in their
own development plans. Databases and DBMSs can be categorized according to the
database model(s) that they support (such as relational or XML), the type(s) of
computer they run on (from a server cluster to a mobile phone), the query language(s)
used to access the database (such as SQL or XQuery),
and their internal engineering, which affects performance, scalability,
resilience, and security.
DBMSs comprise a significant market,
computer and storage vendors often take into account DBMS requirements in their
own development plans. Databases and DBMSs can be categorized according to the
database model(s) that they support (such as relational or XML), the type(s) of
computer they run on (from a server cluster to a mobile phone), the query language(s)
used to access the database (such as SQL or XQuery),
and their internal engineering, which affects performance, scalability,
resilience, and security.
Historical
development of database
development of database
Following
the technology
progress in the areas of processors, computer memory, computer storage,
and computer networks,
the sizes, capabilities, and performance of databases and their respective
DBMSs have grown in orders of magnitude. The development of database technology
can be divided into three eras based on data model or structure: navigational,
SQL/relational,
and post-relational.
the technology
progress in the areas of processors, computer memory, computer storage,
and computer networks,
the sizes, capabilities, and performance of databases and their respective
DBMSs have grown in orders of magnitude. The development of database technology
can be divided into three eras based on data model or structure: navigational,
SQL/relational,
and post-relational.
The
relational model,
first proposed in 1970 by Edgar F. Codd,
departed from this tradition by insisting that applications should search for
data by content, rather than by following links. The relational model employs
sets of ledger-style tables, each used for a different type of entity. Only in
the mid-1980s did computing hardware become powerful enough to allow the wide
deployment of relational systems (DBMSs plus applications). By the early 1990s,
however, relational systems dominated in all large-scale data processing
applications, and as of 2015 they remain dominant: IBM DB2, Oracle, MySQL, and Microsoft
SQL Server are the top DBMS.
The dominant database language, standardised SQL for the relational model, has
influenced database languages for other data models.
relational model,
first proposed in 1970 by Edgar F. Codd,
departed from this tradition by insisting that applications should search for
data by content, rather than by following links. The relational model employs
sets of ledger-style tables, each used for a different type of entity. Only in
the mid-1980s did computing hardware become powerful enough to allow the wide
deployment of relational systems (DBMSs plus applications). By the early 1990s,
however, relational systems dominated in all large-scale data processing
applications, and as of 2015 they remain dominant: IBM DB2, Oracle, MySQL, and Microsoft
SQL Server are the top DBMS.
The dominant database language, standardised SQL for the relational model, has
influenced database languages for other data models.
Object databases
were developed in the 1980s to overcome the inconvenience of object-relational impedance mismatch,
which led to the coining of the term “post-relational” and also the
development of hybrid object-relational
databases.
were developed in the 1980s to overcome the inconvenience of object-relational impedance mismatch,
which led to the coining of the term “post-relational” and also the
development of hybrid object-relational
databases.
The
next generation of post-relational databases in the late 2000s became known as NoSQL databases, introducing fast key-value stores
and document-oriented
databases. A competing “next generation” known as NewSQL
databases attempted new implementations that retained the relational/SQL model
while aiming to match the high performance of NoSQL compared to commercially
available relational DBMSs.
next generation of post-relational databases in the late 2000s became known as NoSQL databases, introducing fast key-value stores
and document-oriented
databases. A competing “next generation” known as NewSQL
databases attempted new implementations that retained the relational/SQL model
while aiming to match the high performance of NoSQL compared to commercially
available relational DBMSs.
Languages used in database
Database languages are special-purpose languages,
which do one or more of the following:
which do one or more of the following:
·
Query language – allows
searching for information and computing derived information
Query language – allows
searching for information and computing derived information
·
Data definition language – defines data types such as
creating, altering, or dropping and the relationships among them
Data definition language – defines data types such as
creating, altering, or dropping and the relationships among them
·
Data manipulation language – performs tasks such as
inserting, updating, or deleting data occurrences
Data manipulation language – performs tasks such as
inserting, updating, or deleting data occurrences
Database languages are specific to a particular
data model. Notable examples include:
data model. Notable examples include:
- OQL is
an object model language standard (from the Object Data Management Group).
It has influenced the design of some of the newer query languages like JDOQL and
EJB QL. - XQuery is
a standard XML query language implemented by XML database systems such as MarkLogic and
eXist, by
relational databases with XML capability such as Oracle and DB2, and also
by in-memory XML processors such as Saxon. - SQL/XML
combines XQuery
with SQL. - SQL
combines the roles of data definition, data manipulation, and query in a
single language. It was one of the first commercial languages for the
relational model, although it departs in some respects from the relational model as described by Codd
(for example, the rows and columns of a table can be ordered). SQL became
a standard of the American National Standards Institute
(ANSI) in 1986, and of the International
Organization for Standardization
(ISO) in 1987. The standards have been regularly enhanced since and is
supported (with varying degrees of conformance) by all mainstream
commercial relational DBMSs.
Object-oriented database
Items created using object-oriented
programming languages are often stored in relational
databases, but object-oriented databases are well-suited for those items.
programming languages are often stored in relational
databases, but object-oriented databases are well-suited for those items.
An object-oriented database is organized
around objects rather than actions, and data rather than logic. For example, a
multimedia record in a relational database can be a definable data object, as
opposed to an alphanumeric value.
around objects rather than actions, and data rather than logic. For example, a
multimedia record in a relational database can be a definable data object, as
opposed to an alphanumeric value.
Graph database
A graph-oriented database, or graph database, is a
type of NoSQL database that uses graph theory to
store, map and query relationships. Graph databases are basically collections
of nodes and edges, where each node represents an entity, and each edge
represents a connection between nodes.
type of NoSQL database that uses graph theory to
store, map and query relationships. Graph databases are basically collections
of nodes and edges, where each node represents an entity, and each edge
represents a connection between nodes.
Graph databases are growing in popularity for
analyzing interconnections. For example, companies might use a graph database
to mine data about
customers from social media.
analyzing interconnections. For example, companies might use a graph database
to mine data about
customers from social media.
Accessing the database: DBMS and
RDBMS
RDBMS
A database management
system (DBMS) is a type of software that allows you to
define, manipulate, retrieve and manage data stored within a database. A relational database
management system (RDBMS) is a type of database management software
that was developed in the 1970s, based on the relational model, and is still
the most popular way to manage a database. DBMS technologies began in the 1960s
to support hierarchical databases, and they include IBM’s Information
Management System and CA’s Integrated Database Management System.
system (DBMS) is a type of software that allows you to
define, manipulate, retrieve and manage data stored within a database. A relational database
management system (RDBMS) is a type of database management software
that was developed in the 1970s, based on the relational model, and is still
the most popular way to manage a database. DBMS technologies began in the 1960s
to support hierarchical databases, and they include IBM’s Information
Management System and CA’s Integrated Database Management System.
Relational database
A relational database, is a
tabular database in which data is defined so that it can be reorganized and
accessed in a number of different ways.
tabular database in which data is defined so that it can be reorganized and
accessed in a number of different ways.
Relational databases are made up of a set of tables
with data that fits into a predefined category. Each table has at least one
data category in a column, and each row has a certain data instance for the
categories which are defined in the columns.
with data that fits into a predefined category. Each table has at least one
data category in a column, and each row has a certain data instance for the
categories which are defined in the columns.
The Structured Query
Language (SQL) is the standard user and application program
interface for a relational database. Relational databases are easy to extend,
and a new data category can be added after the original database creation
without requiring that you modify all the existing applications.
Language (SQL) is the standard user and application program
interface for a relational database. Relational databases are easy to extend,
and a new data category can be added after the original database creation
without requiring that you modify all the existing applications.
Distributed database
A distributed database is a database in which
portions of the database are stored in multiple physical locations, and in
which processing is dispersed or replicated among different points in a
network.
portions of the database are stored in multiple physical locations, and in
which processing is dispersed or replicated among different points in a
network.
Distributed databases can be homogeneous or
heterogeneous. All the physical locations in a homogeneous distributed database
system have the same underlying hardware and run the same
operating systems and database applications. The hardware, operating systems or
database applications in a heterogeneous distributed database may be different
at each of the locations.
heterogeneous. All the physical locations in a homogeneous distributed database
system have the same underlying hardware and run the same
operating systems and database applications. The hardware, operating systems or
database applications in a heterogeneous distributed database may be different
at each of the locations.
Cloud database
A cloud database is a database that has been
optimized or built for a virtualized environment, either in a hybrid cloud,
public cloud or private cloud. Cloud databases provide benefits such as the
ability to pay for storage capacity and bandwidth on a per-use basis, and they
provide scalability on demand, along with high availability.
optimized or built for a virtualized environment, either in a hybrid cloud,
public cloud or private cloud. Cloud databases provide benefits such as the
ability to pay for storage capacity and bandwidth on a per-use basis, and they
provide scalability on demand, along with high availability.
A cloud database also gives enterprises the
opportunity to support business applications in a software-as-a-service
deployment.
opportunity to support business applications in a software-as-a-service
deployment.
NoSQL database
NoSQL databases are effective for big data
performance issues that relational databases aren’t built to solve. They are
most effective when an organization must analyze large chunks of unstructured data or
data that’s stored across multiple virtual servers in the
cloud.
performance issues that relational databases aren’t built to solve. They are
most effective when an organization must analyze large chunks of unstructured data or
data that’s stored across multiple virtual servers in the
cloud.
