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  1. A database (collection of organized information in a regular structure).
    The Data Bank's primary role is to provide scientists in member countries with reliable nuclear data and computer programs for use in different nuclear applications.
  2. An organization dedicated to maintaining a database.



Extensive Definition

A database is a structured collection of records or data. A computer database relies upon software to organize the storage of data. The software models the database structure in what are known as database models. The model in most common use today is the relational model. Other models such as the hierarchical model and the network model use a more explicit representation of relationships (see below for explanation of the various database models).
Database management systems (DBMS) are the software used to organize and maintain the database. These are categorized according to the database model that they support. The model tends to determine the query languages that are available to access the database. A great deal of the internal engineering of a DBMS, however, is independent of the data model, and is concerned with managing factors such as performance, concurrency, integrity, and recovery from hardware failures. In these areas there are large differences between products.


The earliest known use of the term data base was in November 1963, when the System Development Corporation sponsored a symposium under the title Development and Management of a Computer-centered Data Base. Database as a single word became common in Europe in the early 1970s and by the end of the decade it was being used in major American newspapers. (The abbreviation DB, however, survives.)
The first database management systems were developed in the 1960s. A pioneer in the field was Charles Bachman. Bachman's early papers show that his aim was to make more effective use of the new direct access storage devices becoming available: until then, data processing had been based on punched cards and magnetic tape, so that serial processing was the dominant activity. Two key data models arose at this time: CODASYL developed the network model based on Bachman's ideas, and (apparently independently) the hierarchical model was used in a system developed by North American Rockwell later adopted by IBM as the cornerstone of their IMS product. While IMS along with the CODASYL IDMS were the big, high visibility databases developed in the 1960s, several others were also born in that decade, some of which have a significant installed base today. Two worthy of mention are the PICK and MUMPS databases, with the former developed originally as an operating system with an embedded database and the latter as a programming language and database for the development of healthcare systems.
The relational model was proposed by E. F. Codd in 1970. He criticized existing models for confusing the abstract description of information structure with descriptions of physical access mechanisms. For a long while, however, the relational model remained of academic interest only. While CODASYL products (IDMS) and network model products (IMS) were conceived as practical engineering solutions taking account of the technology as it existed at the time, the relational model took a much more theoretical perspective, arguing (correctly) that hardware and software technology would catch up in time. Among the first implementations were Michael Stonebraker's Ingres at Berkeley, and the System R project at IBM. Both of these were research prototypes, announced during 1976. The first commercial products, Oracle and DB2, did not appear until around 1980. The first successful database product for microcomputers was dBASE for the CP/M and PC-DOS/MS-DOS operating systems.
During the 1980s, research activity focused on distributed database systems and database machines. Another important theoretical idea was the Functional Data Model, but apart from some specialized applications in genetics, molecular biology, and fraud investigation, the world took little notice.
In the 1990s, attention shifted to object-oriented databases. These had some success in fields where it was necessary to handle more complex data than relational systems could easily cope with, such as spatial databases, engineering data (including software repositories), and multimedia data. Some of these ideas were adopted by the relational vendors, who integrated new features into their products as a result. The 1990s also saw the spread of Open Source databases, such as PostgreSQL and MySQL.
In the 2000s, the fashionable area for innovation is the XML database. As with object databases, this has spawned a new collection of start-up companies, but at the same time the key ideas are being integrated into the established relational products. XML databases aim to remove the traditional divide between documents and data, allowing all of an organization's information resources to be held in one place, whether they are highly structured or not.

Database models

Various techniques are used to model data structure. Most database systems are built around one particular data model, although it is increasingly common for products to offer support for more than one model. For any one logical model various physical implementations may be possible, and most products will offer the user some level of control in tuning the physical implementation, since the choices that are made have a significant effect on performance. Here are three examples:

Hierarchical model

In a hierarchical model, data is organized into an inverted tree-like structure, implying a multiple downward link in each node to describe the nesting, and a sort field to keep the records in a particular order in each same-level list. This structure arranges the various data elements in a hierarchy and helps to establish logical relationships among data elements of multiple files. Each unit in the model is a record which is also known as a node. In such a model, each record on one level can be related to multiple records on the next lower level. A record that has subsidiary records is called a parent and the subsidiary records are called children. Data elements in this model are well suited for one-to-many relationships with other data elements in the database.
This model is advantageous when the data elements are inherently hierarchical. The disadvantage is that in order to prepare the database it becomes necessary to identify the requisite groups of files that are to be logically integrated. Hence, a hierarchical data model may not always be flexible enough to accommodate the dynamic needs of an organisation.

Network model

The network model tends to store records with links to other records. Each record in the database can have multiple parents, i.e., the relationships among data elements can have a many to many relationship. Associations are tracked via "pointers". These pointers can be node numbers or disk addresses. Most network databases tend to also include some form of hierarchical model. Databases can be translated from hierarchical model to network and vice versa. The main difference between the network model and hierarchical model is that in a network model, a child can have a number of parents whereas in a hierarchical model, a child can have only one parent.
The network model provides greater advantage than the hierarchical model in that it promotes greater flexibility and data accessibility, since records at a lower level can be accessed without accessing the records above them. This model is more efficient than hierarchical model, easier to understand and can be applied to many real world problems that require routine transactions. The disadvantages are that: It is a complex process to design and develop a network database; It has to be refined frequently; It requires that the relationships among all the records be defined before development starts, and changes often demand major programming efforts; Operation and maintenance of the network model is expensive and time consuming.
Examples of database engines that have network model capabilities are RDM Embedded and RDM Server.

Relational model

The basic data structure of the relational model is a table where information about a particular entity (say, an employee) is represented in columns and rows. The columns enumerate the various attributes of an entity (e.g. employee_name, address, phone_number). Rows (also called records) represent instances of an entity (e.g. specific employees).
The "relation" in "relational database" comes from the mathematical notion of relations from the field of set theory. A relation is a set of tuples, so rows are sometimes called tuples. All tables in a relational database adhere to three basic rules.
  • The ordering of columns is immaterial
  • Identical rows are not allowed in a table
  • Each row has a single (separate) value for each of its columns (each tuple has an atomic value).
If the same value occurs in two different records (from the same table or different tables) it can imply a relationship between those records. Relationships between records are often categorized by their cardinality (1:1, (0), 1:M, M:M).
Tables can have a designated column or set of columns that act as a "key" to select rows from that table with the same or similar key values. A "primary key" is a key that has a unique value for each row in the table. Keys are commonly used to join or combine data from two or more tables. For example, an employee table may contain a column named address which contains a value that matches the key of a address table. Keys are also critical in the creation of indexes, which facilitate fast retrieval of data from large tables. It is not necessary to define all the keys in advance; a column can be used as a key even if it was not originally intended to be one.

Relational operations

Users (or programs) request data from a relational database by sending it a query that is written in a special language, usually a dialect of SQL. Although SQL was originally intended for end-users, it is much more common for SQL queries to be embedded into software that provides an easier user interface. Many web applications, such as Wikipedia, perform SQL queries when generating pages.
In response to a query, the database returns a result set, which is the list of rows constituting the answer. The simplest query is just to return all the rows from a table, but more often, the rows are filtered in some way to return just the answer wanted. Often, data from multiple tables are combined into one, by doing a join. There are a number of relational operations in addition to join.

Normal forms

Relations are classified based upon the types of anomalies to which they're vulnerable. A database that's in the first normal form is vulnerable to all types of anomalies, while a database that's in the domain/key normal form has no modification anomalies. Normal forms are hierarchical in nature. That is, the lowest level is the first normal form, and the database cannot meet the requirements for higher level normal forms without first having met all the requirements of the lesser normal form.

Database Management Systems

Relational database management systems

An RDBMS implements the features of the relational model outlined above. In this context, Date's Information Principle states:
The entire information content of the database is represented in one and only one way. Namely as explicit values in column positions (attributes) and rows in relations (tuples) Ergo, there are no explicit pointers between related tables.

Post-relational database models

Several products have been identified as post-relational because the data model incorporates relations but is not constrained by the Information Principle, requiring that all information is represented by data values in relations. Products using a post-relational data model typically employ a model that actually pre-dates the relational model. These might be identified as a directed graph with trees on the nodes.
Examples of models that could be classified as post-relational are PICK aka MultiValue, and MUMPS.

Object database models

In recent years, the object-oriented paradigm has been applied to database technology, creating a new programming model known as object databases. These databases attempt to bring the database world and the application programming world closer together, in particular by ensuring that the database uses the same type system as the application program. This aims to avoid the overhead (sometimes referred to as the impedance mismatch) of converting information between its representation in the database (for example as rows in tables) and its representation in the application program (typically as objects). At the same time, object databases attempt to introduce the key ideas of object programming, such as encapsulation and polymorphism, into the world of databases.
A variety of these ways have been tried for storing objects in a database. Some products have approached the problem from the application programming end, by making the objects manipulated by the program persistent. This also typically requires the addition of some kind of query language, since conventional programming languages do not have the ability to find objects based on their information content. Others have attacked the problem from the database end, by defining an object-oriented data model for the database, and defining a database programming language that allows full programming capabilities as well as traditional query facilities.

DBMS internals

Storage and physical database design

Database tables/indexes are typically stored in memory or on hard disk in one of many forms, ordered/unordered flat files, ISAM, heaps, hash buckets or B+ trees. These have various advantages and disadvantages discussed further in the main article on this topic. The most commonly used are B+ trees and ISAM.
Other important design choices relate to the clustering of data by category (such as grouping data by month, or location), creating pre-computed views known as materialized views, partitioning data by range or hash. As well memory management and storage topology can be important design choices for database designers. Just as normalization is used to reduce storage requirements and improve the extensibility of the database, conversely denormalization is often used to reduce join complexity and reduce execution time for queries.


All of these databases can take advantage of indexing to increase their speed, and this technology has advanced tremendously since its early uses in the 1960s and 1970s. The most common kind of index is a sorted list of the contents of some particular table column, with pointers to the row associated with the value. An index allows a set of table rows matching some criterion to be located quickly. Typically, indexes are also stored in the various forms of data-structure mentioned above (such as B-trees, hashes, and linked lists). Usually, a specific technique is chosen by the database designer to increase efficiency in the particular case of the type of index required.
Relational DBMS's have the advantage that indexes can be created or dropped without changing existing applications making use of it. The database chooses between many different strategies based on which one it estimates will run the fastest. In other words, indexes are transparent to the application or end-user querying the database; while they affect performance, any SQL command will run with or without index to compute the result of an SQL statement. The RDBMS will produce a plan of how to execute the query, which is generated by analyzing the run times of the different algorithms and selecting the quickest. Some of the key algorithms that deal with joins are nested loop join, sort-merge join and hash join. Which of these is chosen depends on whether an index exists, what type it is, and its cardinality.
An index speeds up access to data, but it has disadvantages as well. First, every index increases the amount of storage on the hard drive necessary for the database file, and second, the index must be updated each time the data are altered, and this costs time. (Thus an index saves time in the reading of data, but it costs time in entering and altering data. It thus depends on the use to which the data are to be put whether an index is on the whole a net plus or minus in the quest for efficiency.)
A special case of an index is a primary index, or primary key, which is distinguished in that the primary index must ensure a unique reference to a record. Often, for this purpose one simply uses a running index number (ID number). Primary indexes play a significant role in relational databases, and they can speed up access to data considerably.

Transactions and concurrency

In addition to their data model, most practical databases ("transactional databases") attempt to enforce a database transaction . Ideally, the database software should enforce the ACID rules, summarized here:
  • Atomicity: Either all the tasks in a transaction must be done, or none of them. The transaction must be completed, or else it must be undone (rolled back).
  • Consistency: Every transaction must preserve the integrity constraints — the declared consistency rules — of the database. It cannot place the data in a contradictory state.
  • Isolation: Two simultaneous transactions cannot interfere with one another. Intermediate results within a transaction are not visible to other transactions.
  • Durability: Completed transactions cannot be aborted later or their results discarded. They must persist through (for instance) restarts of the DBMS after crashes
In practice, many DBMS's allow most of these rules to be selectively relaxed for better performance.
Concurrency control is a method used to ensure that transactions are executed in a safe manner and follow the ACID rules. The DBMS must be able to ensure that only serializable, recoverable schedules are allowed, and that no actions of committed transactions are lost while undoing aborted transactions .


Replication of databases is closely related to transactions. If a database can log its individual actions, it is possible to create a duplicate of the data in real time. The duplicate can be used to improve performance or availability of the whole database system. Common replication concepts include:
  • Master/Slave Replication: All write requests are performed on the master and then replicated to the slaves
  • Quorum: The result of Read and Write requests are calculated by querying a "majority" of replicas.
  • Multimaster: Two or more replicas sync each other via a transaction identifier.
Parallel synchronous replication of databases enables transactions to be replicated on multiple servers simultaneously, which provides a method for backup and security as well as data availability.


Database security denotes the system, processes, and procedures that protect a database from unintended activity.
In the United Kingdom legislation protecting the public from unauthorized disclosure of personal information held on databases falls under the Office of the Information Commissioner. United Kingdom based organizations holding personal data in electronic format (databases for example) are required to register with the Data Commissioner. (reference:


Locking is the act of putting a lock (access restriction) on an aspect of a database which at a particular given instance is being modified. Such locks can be applied on a row level, or on other levels such as an entire table. This helps maintain the integrity of the data by ensuring that only one user at a time can modify the data. Databases can also be locked for other reasons, like access restrictions for given levels of user. Databases are also locked for routine database maintenance, which prevents changes being made during the maintenance. See IBM for more detail.


Depending on the intended use, there are a number of database architectures in use. Many databases use a combination of strategies. On-line Transaction Processing systems (OLTP) often use a row-oriented datastore architecture, while data-warehouse and other retrieval-focused applications like Google's BigTable, or bibliographic database(library catalogue) systems may use a column-oriented datastore architecture.
Document-Oriented, XML, Knowledgebases, as well as frame databases and rdf-stores (aka Triple-Stores), may also use a combination of these architectures in their implementation.
Finally it should be noted that not all database have or need a database 'schema' (so called schema-less databases).

Applications of databases

Databases are used in many applications, spanning virtually the entire range of computer software. Databases are the preferred method of storage for large multiuser applications, where coordination between many users is needed. Even individual users find them convenient, and many electronic mail programs and personal organizers are based on standard database technology. Software database drivers are available for most database platforms so that application software can use a common Application Programming Interface to retrieve the information stored in a database. Two commonly used database APIs are JDBC and ODBC.
For example suppliers database contains the data relating to suppliers such as;
  • supplier name
  • supplier code
  • supplier address
It is often used by schools to teach students and grade them.

Database as Cultural Form

Although originally a computer technology, the database, according to media theorist Lev Manovich, is becoming a new cultural form in its own right and a genre of new media. A cultural form is one of many ways that people represent the world—art and literature, for example. As contemporary culture is gradually computerized, Manovich argues, traditional cultural forms are being replaced with new ones that derive from the computer. He calls this transcoding. The database is the computer age’s key form of cultural expression, as narrative was to the modern age via cinema. In this analysis, he is using "database" metaphorically. See also database cinema.
Katherine Hayles has argued, in response, that narrative and database are not in opposition but rather are natural symbionts.


  • Connolly, Thomas, and Caroln Begg. Database Systems. New York: Harlow, 2002.
  • Date, C. J. An Introduction to Database Systems, Eighth Edition, Addison Wesley, 2003.
  • Galindo, J., Urrutia, A., Piattini, M., Fuzzy Databases: Modeling, Design and Implementation (FSQL guide). Idea Group Publishing Hershey, USA, 2006.
  • Galindo, J., Ed. Handbook on Fuzzy Information Processing in Databases. Hershey, PA: Information Science Reference (an imprint of Idea Group Inc.), 2008.
  • Gray, J. and Reuter, A. Transaction Processing: Concepts and Techniques, 1st edition, Morgan Kaufmann Publishers, 1992.
  • Kroenke, David M. Database Processing: Fundamentals, Design, and Implementation (1997), Prentice-Hall, Inc., pages 130-144.
  • Kroenke, David M., and David J. Auer. Database Concepts. 3rd ed. New York: Prentice, 2007.
  • Lightstone, S., T. Teorey, and T. Nadeau, Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more, Morgan Kaufmann Press, 2007. ISBN 0-12369-389-6.
  • Shih, J. "Why Synchronous Parallel Transaction Replication is Hard, But Inevitable?", white paper, 2007.
  • Teorey, T.; Lightstone, S. and Nadeau, T. Database Modeling & Design: Logical Design, 4th edition, Morgan Kaufmann Press, 2005. ISBN 0-12-685352-5
  • Tukey, John W. Exploratory Data Analysis. Reading, MA: Addison Wesley, 1977.

See also

External links

databank in Afrikaans: Databasis
databank in Arabic: قاعدة بيانات
databank in Azerbaijani: Verilənlər bazası
databank in Belarusian: База дадзеных
databank in Belarusian (Tarashkevitsa): База дадзеных
databank in Bosnian: Baza podataka
databank in Breton: Stlennvon
databank in Bulgarian: База данни
databank in Catalan: Base de dades
databank in Czech: Databáze
databank in Danish: Database
databank in German: Datenbank
databank in Estonian: Andmebaas
databank in Modern Greek (1453-): Βάση δεδομένων
databank in Spanish: Base de datos
databank in Esperanto: Datumbazo
databank in Basque: Datu-base
databank in Persian: دادگان
databank in French: Base de données
databank in Irish: Bunachar sonraí
databank in Galician: Base de datos
databank in Korean: 데이터베이스
databank in Hindi: डेटाबेस
databank in Croatian: Baza podataka
databank in Indonesian: Basis data
databank in Interlingua (International Auxiliary Language Association): Base de datos
databank in Icelandic: Gagnagrunnur
databank in Italian: Database
databank in Hebrew: בסיס נתונים
databank in Georgian: მონაცემთა ბაზა
databank in Latvian: Datu bāze
databank in Lithuanian: Duomenų bazė
databank in Hungarian: Adatbázis
databank in Malayalam: ഡാറ്റാബേസ്
databank in Malay (macrolanguage): Pangkalan data
databank in Dutch: Database
databank in Japanese: データベース
databank in Norwegian: Database
databank in Uzbek: Ma'lumotlar Bazasi
databank in Polish: Baza danych
databank in Portuguese: Banco de dados
databank in Romanian: Bază de date
databank in Russian: База данных
databank in Albanian: Baza e të dhënave
databank in Sinhala: දත්ත සමුදාය
databank in Simple English: Database
databank in Slovak: Databáza
databank in Slovenian: Podatkovna baza
databank in Serbian: База података
databank in Finnish: Tietokanta
databank in Swedish: Databas
databank in Tagalog: Database
databank in Tamil: தரவுத்தளம்
databank in Thai: ฐานข้อมูล
databank in Vietnamese: Cơ sở dữ liệu
databank in Turkish: Veri tabanı
databank in Ukrainian: База даних
databank in Chinese: 数据库
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