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org/wiki/Database
In computing, a database is an organized collection of data or a type of data store based on
the use of a database management system (DBMS), the software that interacts with end
users, applications, and the database itself to capture and analyze the data. The DBMS
additionally encompasses the core facilities provided to administer the database. The sum
total of the database, the DBMS and the associated applications can be referred to as a
database system. Often the term "database" is also used loosely to refer to any of the DBMS,
the database system or an application associated with the database.

Small databases can be stored on a file system, while large databases are hosted on
computer clusters or cloud storage. The design of databases spans formal techniques and
practical considerations, including data modeling, efficient data representation and storage,
query languages, security and privacy of sensitive data, and distributed computing issues,
including supporting concurrent access and fault tolerance.

Computer scientists may classify database management systems according to the database
models that they support. Relational databases became dominant in the 1980s. These model
data as rows and columns in a series of tables, and the vast majority use SQL for writing and
querying data. In the 2000s, non-relational databases became popular, collectively referred
to as NoSQL, because they use different query languages.

Formally, a "database" refers to a set of related data accessed through the use of a "database
management system" (DBMS), which is 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) as size and
usage requirements typically necessitate use of a database management system.[1]
Existing DBMSs provide various functions that allow management of a database and its data
which can be classified into four main functional groups:

Both a database and its DBMS conform to the principles of a particular database model.[5]
"Database system" refers collectively to the database model, database management system,
and database.[6]

Physically, database servers are dedicated computers that hold the actual databases and run
only the DBMS and related software. Database servers are usually multiprocessor
computers, with generous memory and RAID disk arrays used for stable storage. 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.[citation needed]

Since DBMSs comprise a significant market, computer and storage vendors often take into
account DBMS requirements in their own development plans.[7]

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.

The sizes, capabilities, and performance of databases and their respective DBMSs have
grown in orders of magnitude. These performance increases were enabled by the
technology progress in the areas of processors, computer memory, computer storage, and
computer networks. The concept of a database was made possible by the emergence of
direct access storage media such as magnetic disks, which became widely available in the
mid-1960s; earlier systems relied on sequential storage of data on magnetic tape. The
subsequent development of database technology can be divided into three eras based on
data model or structure: navigational,[8] SQL/relational, and post-relational.

The two main early navigational data models were the hierarchical model and the CODASYL
model (network model). These were characterized by the use of pointers (often physical
disk addresses) to follow relationships from one record to another.
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 2018[update] they remain dominant: IBM Db2, Oracle, MySQL, and Microsoft SQL
Server are the most searched DBMS.[9] The dominant database language, standardized SQL
for the relational model, has influenced database languages for other data models.[citation
needed]

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.

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.

The introduction of the term database coincided with the availability of direct-access
storage (disks and drums) from the mid-1960s onwards. The term represented a contrast
with the tape-based systems of the past, allowing shared interactive use rather than daily
batch processing. The Oxford English Dictionary cites a 1962 report by the System
Development Corporation of California as the first to use the term "data-base" in a specific
technical sense.[10]

As computers grew in speed and capability, a number of general-purpose database systems

emerged; by the mid-1960s a number of such systems had come into commercial use.
Interest in a standard began to grow, and Charles Bachman, author of one such product, the
Integrated Data Store (IDS), founded the Database Task Group within CODASYL, the group
responsible for the creation and standardization of COBOL. In 1971, the Database Task
Group delivered their standard, which generally became known as the CODASYL approach,
and soon a number of commercial products based on this approach entered the market.
The CODASYL approach offered applications the ability to navigate around a linked data set
which was formed into a large network. Applications could find records by one of three
methods:

Later systems added B-trees to provide alternate access paths. Many CODASYL databases
also added a declarative query language for end users (as distinct from the navigational
API). However, CODASYL databases were complex and required significant training and
effort to produce useful applications.

IBM also had its own DBMS in 1966, known as Information Management System (IMS). IMS
was a development of software written for the Apollo program on the System/360. IMS was
generally similar in concept to CODASYL, but used a strict hierarchy for its model of data
navigation instead of CODASYL's network model. Both concepts later became known as
navigational databases due to the way data was accessed: the term was popularized by
Bachman's 1973 Turing Award presentation The Programmer as Navigator. IMS is classified
by IBM as a hierarchical database. IDMS and Cincom Systems' TOTAL databases are
classified as network databases. IMS remains in use as of 2014[update].[11]

Edgar F. Codd worked at IBM in San Jose, California, in one of their offshoot offices that
were primarily involved in the development of hard disk systems. He was unhappy with the
navigational model of the CODASYL approach, notably the lack of a "search" facility. In 1970,
he wrote a number of papers that outlined a new approach to database construction that
eventually culminated in the groundbreaking A Relational Model of Data for Large Shared
Data Banks.[12]

In this paper, he described a new system for storing and working with large databases.
Instead of records being stored in some sort of linked list of free-form records as in
CODASYL, Codd's idea was to organize the data as a number of "tables", each table being
used for a different type of entity. Each table would contain a fixed number of columns
containing the attributes of the entity. One or more columns of each table were designated
as a primary key by which the rows of the table could be uniquely identified; cross-
references between tables always used these primary keys, rather than disk addresses, and
queries would join tables based on these key relationships, using a set of operations based
on the mathematical system of relational calculus (from which the model takes its name).
Splitting the data into a set of normalized tables (or relations) aimed to ensure that each
"fact" was only stored once, thus simplifying update operations. Virtual tables called views
could present the data in different ways for different users, but views could not be directly
updated.
Codd used mathematical terms to define the model: relations, tuples, and domains rather
than tables, rows, and columns. The terminology that is now familiar came from early
implementations. Codd would later criticize the tendency for practical implementations to
depart from the mathematical foundations on which the model was based.

The use of primary keys (user-oriented identifiers) to represent cross-table relationships,

rather than disk addresses, had two primary motivations. From an engineering perspective,
it enabled tables to be relocated and resized without expensive database reorganization.
But Codd was more interested in the difference in semantics: the use of explicit identifiers
made it easier to define update operations with clean mathematical definitions, and it also
enabled query operations to be defined in terms of the established discipline of first-order
predicate calculus; because these operations have clean mathematical properties, it
becomes possible to rewrite queries in provably correct ways, which is the basis of query
optimization. There is no loss of expressiveness compared with the hierarchic or network
models, though the connections between tables are no longer so explicit.

In the hierarchic and network models, records were allowed to have a complex internal
structure. For example, the salary history of an employee might be represented as a
"repeating group" within the employee record. In the relational model, the process of
normalization led to such internal structures being replaced by data held in multiple tables,
connected only by logical keys.

For instance, a common use of a database system is to track information about users, their
name, login information, various addresses and phone numbers. In the navigational
approach, all of this data would be placed in a single variable-length record. In the relational
approach, the data would be normalized into a user table, an address table and a phone
number table (for instance). Records would be created in these optional tables only if the
address or phone numbers were actually provided.

As well as identifying rows/records using logical identifiers rather than disk addresses,
Codd changed the way in which applications assembled data from multiple records. Rather
than requiring applications to gather data one record at a time by navigating the links, they
would use a declarative query language that expressed what data was required, rather than
the access path by which it should be found. Finding an efficient access path to the data
became the responsibility of the database management system, rather than the application
programmer. This process, called query optimization, depended on the fact that queries
were expressed in terms of mathematical logic.
Codd's paper was picked up by two people at Berkeley, Eugene Wong and Michael
Stonebraker. They started a project known as INGRES using funding that had already been
allocated for a geographical database project and student programmers to produce code.
Beginning in 1973, INGRES delivered its first test products which were generally ready for
widespread use in 1979. INGRES was similar to System R in a number of ways, including the
use of a "language" for data access, known as QUEL. Over time, INGRES moved to the
emerging SQL standard.

IBM itself did one test implementation of the relational model, PRTV, and a production one,
Business System 12, both now discontinued. Honeywell wrote MRDS for Multics, and now
there are two new implementations: Alphora Dataphor and Rel. Most other DBMS
implementations usually called relational are actually SQL DBMSs.

In 1970, the University of Michigan began development of the MICRO Information

Management System[13] based on D.L. Childs' Set-Theoretic Data model.[14][15][16]
MICRO was used to manage very large data sets by the US Department of Labor, the U.S.
Environmental Protection Agency, and researchers from the University of Alberta, the
University of Michigan, and Wayne State University. It ran on IBM mainframe computers
using the Michigan Terminal System.[17] The system remained in production until 1998.

In the 1970s and 1980s, attempts were made to build database systems with integrated
hardware and software. The underlying philosophy was that such integration would
provide higher performance at a lower cost. Examples were IBM System/38, the early
offering of Teradata, and the Britton Lee, Inc. database machine.

Another approach to hardware support for database management was ICL's CAFS
accelerator, a hardware disk controller with programmable search capabilities. In the long
term, these efforts were generally unsuccessful because specialized database machines
could not keep pace with the rapid development and progress of general-purpose
computers. Thus most database systems nowadays are software systems running on
general-purpose hardware, using general-purpose computer data storage. However, this
idea is still pursued in certain applications by some companies like Netezza and Oracle
(Exadata).

IBM started working on a prototype system loosely based on Codd's concepts as System R in
the early 1970s. The first version was ready in 1974/5, and work then started on multi-
table systems in which the data could be split so that all of the data for a record (some of
which is optional) did not have to be stored in a single large "chunk". Subsequent multi-user
versions were tested by customers in 1978 and 1979, by which time a standardized query
language – SQL[citation needed] – had been added. Codd's ideas were establishing
themselves as both workable and superior to CODASYL, pushing IBM to develop a true
production version of System R, known as SQL/DS, and, later, Database 2 (IBM Db2).

Larry Ellison's Oracle Database (or more simply, Oracle) started from a different chain,
based on IBM's papers on System R. Though Oracle V1 implementations were completed in
1978, it was not until Oracle Version 2 when Ellison beat IBM to market in 1979.[18]

Stonebraker went on to apply the lessons from INGRES to develop a new database, Postgres,
which is now known as PostgreSQL. PostgreSQL is often used for global mission-critical
applications (the .org and .info domain name registries use it as their primary data store, as
do many large companies and financial institutions).

In Sweden, Codd's paper was also read and Mimer SQL was developed in the mid-1970s at
Uppsala University. In 1984, this project was consolidated into an independent enterprise.

Another data model, the entity–relationship model, emerged in 1976 and gained popularity
for database design as it emphasized a more familiar description than the earlier relational
model. Later on, entity–relationship constructs were retrofitted as a data modeling
construct for the relational model, and the difference between the two has become
irrelevant.[citation needed]

The 1980s ushered in the age of desktop computing. The new computers empowered their
users with spreadsheets like Lotus 1-2-3 and database software like dBASE. The dBASE
product was lightweight and easy for any computer user to understand out of the box. C.
Wayne Ratliff, the creator of dBASE, stated: "dBASE was different from programs like BASIC,
C, FORTRAN, and COBOL in that a lot of the dirty work had already been done. The data
manipulation is done by dBASE instead of by the user, so the user can concentrate on what
he is doing, rather than having to mess with the dirty details of opening, reading, and
closing files, and managing space allocation."[19] dBASE was one of the top selling software
titles in the 1980s and early 1990s.

The 1990s, along with a rise in object-oriented programming, saw a growth in how data in
various databases were handled. Programmers and designers began to treat the data in
their databases as objects. That is to say that if a person's data were in a database, that
person's attributes, such as their address, phone number, and age, were now considered to
belong to that person instead of being extraneous data. This allows for relations between
data to be related to objects and their attributes and not to individual fields.[20] The term
"object–relational impedance mismatch" described the inconvenience of translating
between programmed objects and database tables. Object databases and object–relational
databases attempt to solve this problem by providing an object-oriented language
(sometimes as extensions to SQL) that programmers can use as alternative to purely
relational SQL. On the programming side, libraries known as object–relational mappings
(ORMs) attempt to solve the same problem.

XML databases are a type of structured document-oriented database that allows querying
based on XML document attributes. XML databases are mostly used in applications where
the data is conveniently viewed as a collection of documents, with a structure that can vary
from the very flexible to the highly rigid: examples include scientific articles, patents, tax
filings, and personnel records.

NoSQL databases are often very fast, do not require fixed table schemas, avoid join
operations by storing denormalized data, and are designed to scale horizontally.

In recent years, there has been a strong demand for massively distributed databases with
high partition tolerance, but according to the CAP theorem, it is impossible for a distributed
system to simultaneously provide consistency, availability, and partition tolerance
guarantees. A distributed system can satisfy any two of these guarantees at the same time,
but not all three. For that reason, many NoSQL databases are using what is called eventual
consistency to provide both availability and partition tolerance guarantees with a reduced
level of data consistency.

NewSQL is a class of modern relational databases that aims to provide the same scalable
performance of NoSQL systems for online transaction processing (read-write) workloads
while still using SQL and maintaining the ACID guarantees of a traditional database system.

Databases are used to support internal operations of organizations and to underpin online
interactions with customers and suppliers (see Enterprise software).

Databases are used to hold administrative information and more specialized data, such as
engineering data or economic models. Examples include computerized library systems,
flight reservation systems, computerized parts inventory systems, and many content
management systems that store websites as collections of webpages in a database.

One way to classify databases involves the type of their contents, for example: bibliographic,
document-text, statistical, or multimedia objects. Another way is by their application area,
for example: accounting, music compositions, movies, banking, manufacturing, or insurance.
A third way is by some technical aspect, such as the database structure or interface type.
This section lists a few of the adjectives used to characterize different kinds of databases.

Connolly and Begg define database management system (DBMS) as a "software system that
enables users to define, create, maintain and control access to the database."[24] Examples
of DBMS's include MySQL, MariaDB, PostgreSQL, Microsoft SQL Server, Oracle Database, and
Microsoft Access.

The DBMS acronym is sometimes extended to indicate the underlying database model, with
RDBMS for the relational, OODBMS for the object (oriented) and ORDBMS for the object–
relational model. Other extensions can indicate some other characteristics, such as DDBMS
for a distributed database management systems.

The functionality provided by a DBMS can vary enormously. The core functionality is the
storage, retrieval and update of data. Codd proposed the following functions and services a
fully-fledged general purpose DBMS should provide:[25]

It is also generally to be expected the DBMS will provide a set of utilities for such purposes
as may be necessary to administer the database effectively, including import, export,
monitoring, defragmentation and analysis utilities.[26] The core part of the DBMS
interacting between the database and the application interface sometimes referred to as the
database engine.

Often DBMSs will have configuration parameters that can be statically and dynamically
tuned, for example the maximum amount of main memory on a server the database can use.
The trend is to minimize the amount of manual configuration, and for cases such as
embedded databases the need to target zero-administration is paramount.

The large major enterprise DBMSs have tended to increase in size and functionality and
have involved up to thousands of human years of development effort throughout their
lifetime.[a]
Early multi-user DBMS typically only allowed for the application to reside on the same
computer with access via terminals or terminal emulation software. The client–server
architecture was a development where the application resided on a client desktop and the
database on a server allowing the processing to be distributed. This evolved into a multitier
architecture incorporating application servers and web servers with the end user interface
via a web browser with the database only directly connected to the adjacent tier.[28]

A general-purpose DBMS will provide public application programming interfaces (API) and
optionally a processor for database languages such as SQL to allow applications to be
written to interact with and manipulate the database. A special purpose DBMS may use a
private API and be specifically customized and linked to a single application. For example,
an email system performs many of the functions of a general-purpose DBMS such as
message insertion, message deletion, attachment handling, blocklist lookup, associating
messages an email address and so forth however these functions are limited to what is
required to handle email.

External interaction with the database will be via an application program that interfaces
with the DBMS.[29] This can range from a database tool that allows users to execute SQL
queries textually or graphically, to a website that happens to use a database to store and
search information.

A programmer will code interactions to the database (sometimes referred to as a

datasource) via an application program interface (API) or via a database language. The
particular API or language chosen will need to be supported by DBMS, possibly indirectly
via a preprocessor or a bridging API. Some API's aim to be database independent, ODBC
being a commonly known example. Other common API's include JDBC and ADO.NET.

Database languages are special-purpose languages, which allow one or more of the
following tasks, sometimes distinguished as sublanguages:

Database languages are specific to a particular data model. Notable examples include:

A database language may also incorporate features like:

Database storage is the container of the physical materialization of a database. It comprises
the internal (physical) level in the database architecture. It also contains all the information
needed (e.g., metadata, "data about the data", and internal data structures) to reconstruct
the conceptual level and external level from the internal level when needed. Databases as
digital objects contain three layers of information which must be stored: the data, the
structure, and the semantics. Proper storage of all three layers is needed for future
preservation and longevity of the database.[33] Putting data into permanent storage is
generally the responsibility of the database engine a.k.a. "storage engine". Though typically
accessed by a DBMS through the underlying operating system (and often using the
operating systems' file systems as intermediates for storage layout), storage properties and
configuration settings are extremely important for the efficient operation of the DBMS, and
thus are closely maintained by database administrators. A DBMS, while in operation, always
has its database residing in several types of storage (e.g., memory and external storage).
The database data and the additional needed information, possibly in very large amounts,
are coded into bits. Data typically reside in the storage in structures that look completely
different from the way the data look at the conceptual and external levels, but in ways that
attempt to optimize (the best possible) these levels' reconstruction when needed by users
and programs, as well as for computing additional types of needed information from the
data (e.g., when querying the database).

Some DBMSs support specifying which character encoding was used to store data, so
multiple encodings can be used in the same database.

Various low-level database storage structures are used by the storage engine to serialize the
data model so it can be written to the medium of choice. Techniques such as indexing may
be used to improve performance. Conventional storage is row-oriented, but there are also
column-oriented and correlation databases.

Often storage redundancy is employed to increase performance. A common example is

storing materialized views, which consist of frequently needed external views or query
results. Storing such views saves the expensive computing them each time they are needed.
The downsides of materialized views are the overhead incurred when updating them to
keep them synchronized with their original updated database data, and the cost of storage
redundancy.

Occasionally a database employs storage redundancy by database objects replication (with

one or more copies) to increase data availability (both to improve performance of
simultaneous multiple end-user accesses to the same database object, and to provide
resiliency in a case of partial failure of a distributed database). Updates of a replicated
object need to be synchronized across the object copies. In many cases, the entire database
is replicated.

With data virtualization, the data used remains in its original locations and real-time access
is established to allow analytics across multiple sources. This can aid in resolving some
technical difficulties such as compatibility problems when combining data from various
platforms, lowering the risk of error caused by faulty data, and guaranteeing that the
newest data is used. Furthermore, avoiding the creation of a new database containing
personal information can make it easier to comply with privacy regulations. However, with
data virtualization, the connection to all necessary data sources must be operational as
there is no local copy of the data, which is one of the main drawbacks of the approach.[34]

Database security deals with all various aspects of protecting the database content, its
owners, and its users. It ranges from protection from intentional unauthorized database
uses to unintentional database accesses by unauthorized entities (e.g., a person or a
computer program).

Database access control deals with controlling who (a person or a certain computer
program) are allowed to access what information in the database. The information may
comprise specific database objects (e.g., record types, specific records, data structures),
certain computations over certain objects (e.g., query types, or specific queries), or using
specific access paths to the former (e.g., using specific indexes or other data structures to
access information). Database access controls are set by special authorized (by the database
owner) personnel that uses dedicated protected security DBMS interfaces.

This may be managed directly on an individual basis, or by the assignment of individuals

and privileges to groups, or (in the most elaborate models) through the assignment of
individuals and groups to roles which are then granted entitlements. Data security prevents
unauthorized users from viewing or updating the database. Using passwords, users are
allowed access to the entire database or subsets of it called "subschemas". For example, an
employee database can contain all the data about an individual employee, but one group of
users may be authorized to view only payroll data, while others are allowed access to only
work history and medical data. If the DBMS provides a way to interactively enter and
update the database, as well as interrogate it, this capability allows for managing personal
databases.

Data security in general deals with protecting specific chunks of data, both physically (i.e.,
from corruption, or destruction, or removal; e.g., see physical security), or the
interpretation of them, or parts of them to meaningful information (e.g., by looking at the
strings of bits that they comprise, concluding specific valid credit-card numbers; e.g., see
data encryption).

Change and access logging records who accessed which attributes, what was changed, and
when it was changed. Logging services allow for a forensic database audit later by keeping a
record of access occurrences and changes. Sometimes application-level code is used to
record changes rather than leaving this in the database. Monitoring can be set up to attempt
to detect security breaches. Therefore, organizations must take database security seriously
because of the many benefits it provides. Organizations will be safeguarded from security
breaches and hacking activities like firewall intrusion, virus spread, and ransom ware. This
helps in protecting the company's essential information, which cannot be shared with
outsiders at any cause.[35]

Database transactions can be used to introduce some level of fault tolerance and data
integrity after recovery from a crash. A database transaction is a unit of work, typically
encapsulating a number of operations over a database (e.g., reading a database object,
writing, acquiring or releasing a lock, etc.), an abstraction supported in database and also
other systems. Each transaction has well defined boundaries in terms of which
program/code executions are included in that transaction (determined by the transaction's
programmer via special transaction commands).

The acronym ACID describes some ideal properties of a database transaction: atomicity,
consistency, isolation, and durability.

A database built with one DBMS is not portable to another DBMS (i.e., the other DBMS
cannot run it). However, in some situations, it is desirable to migrate a database from one
DBMS to another. The reasons are primarily economical (different DBMSs may have
different total costs of ownership or TCOs), functional, and operational (different DBMSs
may have different capabilities). The migration involves the database's transformation from
one DBMS type to another. The transformation should maintain (if possible) the database
related application (i.e., all related application programs) intact. Thus, the database's
conceptual and external architectural levels should be maintained in the transformation. It
may be desired that also some aspects of the architecture internal level are maintained. A
complex or large database migration may be a complicated and costly (one-time) project by
itself, which should be factored into the decision to migrate. This is in spite of the fact that
tools may exist to help migration between specific DBMSs. Typically, a DBMS vendor
provides tools to help import databases from other popular DBMSs.
After designing a database for an application, the next stage is building the database.
Typically, an appropriate general-purpose DBMS can be selected to be used for this
purpose. A DBMS provides the needed user interfaces to be used by database
administrators to define the needed application's data structures within the DBMS's
respective data model. Other user interfaces are used to select needed DBMS parameters
(like security related, storage allocation parameters, etc.).

When the database is ready (all its data structures and other needed components are
defined), it is typically populated with initial application's data (database initialization,
which is typically a distinct project; in many cases using specialized DBMS interfaces that
support bulk insertion) before making it operational. In some cases, the database becomes
operational while empty of application data, and data are accumulated during its operation.

After the database is created, initialized and populated it needs to be maintained. Various
database parameters may need changing and the database may need to be tuned (tuning)
for better performance; application's data structures may be changed or added, new related
application programs may be written to add to the application's functionality, etc.

Sometimes it is desired to bring a database back to a previous state (for many reasons, e.g.,
cases when the database is found corrupted due to a software error, or if it has been
updated with erroneous data). To achieve this, a backup operation is done occasionally or
continuously, where each desired database state (i.e., the values of its data and their
embedding in database's data structures) is kept within dedicated backup files (many
techniques exist to do this effectively). When it is decided by a database administrator to
bring the database back to this state (e.g., by specifying this state by a desired point in time
when the database was in this state), these files are used to restore that state.

Static analysis techniques for software verification can be applied also in the scenario of
query languages. In particular, the *Abstract interpretation framework has been extended
to the field of query languages for relational databases as a way to support sound
approximation techniques.[36] The semantics of query languages can be tuned according to
suitable abstractions of the concrete domain of data. The abstraction of relational database
systems has many interesting applications, in particular, for security purposes, such as fine-
grained access control, watermarking, etc.

Other DBMS features might include:

Increasingly, there are calls for a single system that incorporates all of these core
functionalities into the same build, test, and deployment framework for database
management and source control. Borrowing from other developments in the software
industry, some market such offerings as "DevOps for database".[37]

The first task of a database designer is to produce a conceptual data model that reflects the
structure of the information to be held in the database. A common approach to this is to
develop an entity–relationship model, often with the aid of drawing tools. Another popular
approach is the Unified Modeling Language. A successful data model will accurately reflect
the possible state of the external world being modeled: for example, if people can have more
than one phone number, it will allow this information to be captured. Designing a good
conceptual data model requires a good understanding of the application domain; it typically
involves asking deep questions about the things of interest to an organization, like "can a
customer also be a supplier?", or "if a product is sold with two different forms of packaging,
are those the same product or different products?", or "if a plane flies from New York to
Dubai via Frankfurt, is that one flight or two (or maybe even three)?". The answers to these
questions establish definitions of the terminology used for entities (customers, products,
flights, flight segments) and their relationships and attributes.

Producing the conceptual data model sometimes involves input from business processes, or
the analysis of workflow in the organization. This can help to establish what information is
needed in the database, and what can be left out. For example, it can help when deciding
whether the database needs to hold historic data as well as current data.

Having produced a conceptual data model that users are happy with, the next stage is to
translate this into a schema that implements the relevant data structures within the
database. This process is often called logical database design, and the output is a logical data
model expressed in the form of a schema. Whereas the conceptual data model is (in theory
at least) independent of the choice of database technology, the logical data model will be
expressed in terms of a particular database model supported by the chosen DBMS. (The
terms data model and database model are often used interchangeably, but in this article we
use data model for the design of a specific database, and database model for the modeling
notation used to express that design).

The most popular database model for general-purpose databases is the relational model, or
more precisely, the relational model as represented by the SQL language. The process of
creating a logical database design using this model uses a methodical approach known as
normalization. The goal of normalization is to ensure that each elementary "fact" is only
recorded in one place, so that insertions, updates, and deletions automatically maintain
consistency.

The final stage of database design is to make the decisions that affect performance,
scalability, recovery, security, and the like, which depend on the particular DBMS. This is
often called physical database design, and the output is the physical data model. A key goal
during this stage is data independence, meaning that the decisions made for performance
optimization purposes should be invisible to end-users and applications. There are two
types of data independence: Physical data independence and logical data independence.
Physical design is driven mainly by performance requirements, and requires a good
knowledge of the expected workload and access patterns, and a deep understanding of the
features offered by the chosen DBMS.

Another aspect of physical database design is security. It involves both defining access
control to database objects as well as defining security levels and methods for the data
itself.

A database model is a type of data model that determines the logical structure of a database
and fundamentally determines in which manner data can be stored, organized, and
manipulated. The most popular example of a database model is the relational model (or the
SQL approximation of relational), which uses a table-based format.

Common logical data models for databases include:

An object–relational database combines the two related structures.

Physical data models include:

Other models include:

Specialized models are optimized for particular types of data:

A database management system provides three views of the database data:

While there is typically only one conceptual and internal view of the data, there can be any
number of different external views. This allows users to see database information in a more
business-related way rather than from a technical, processing viewpoint. For example, a
financial department of a company needs the payment details of all employees as part of the
company's expenses, but does not need details about employees that are in the interest of
the human resources department. Thus different departments need different views of the
company's database.

The three-level database architecture relates to the concept of data independence which
was one of the major initial driving forces of the relational model.[39] The idea is that
changes made at a certain level do not affect the view at a higher level. For example, changes
in the internal level do not affect application programs written using conceptual level
interfaces, which reduces the impact of making physical changes to improve performance.

The conceptual view provides a level of indirection between internal and external. On the
one hand it provides a common view of the database, independent of different external view
structures, and on the other hand it abstracts away details of how the data are stored or
managed (internal level). In principle every level, and even every external view, can be
presented by a different data model. In practice usually a given DBMS uses the same data
model for both the external and the conceptual levels (e.g., relational model). The internal
level, which is hidden inside the DBMS and depends on its implementation, requires a
different level of detail and uses its own types of data structure types.

Database technology has been an active research topic since the 1960s, both in academia
and in the research and development groups of companies (for example IBM Research).
Research activity includes theory and development of prototypes. Notable research topics
have included models, the atomic transaction concept, related concurrency control
techniques, query languages and query optimization methods, RAID, and more.

The database research area has several dedicated academic journals (for example, ACM
Transactions on Database Systems-TODS, Data and Knowledge Engineering-DKE) and
annual conferences (e.g., ACM SIGMOD, ACM PODS, VLDB, IEEE ICDE).