A database is an organized collection of data .  It is the collection of schemas , tables , queries , reports, views , and other objects. The data are Typically Organized to model aspects of reality in a way That support processes Requiring information, Such As modeling the availability of rooms in hotels in a Way That supports finding a hotel with vacancies.
A database management system ( DBMS ) is a software application that interacts with the user, other applications, and the database itself to capture and analyze data. A general-purpose DBMS is designed to allow the definition, creation, querying, update, and administration of databases. Well-known DBMSs include MySQL , PostgreSQL , MongoDB , MariaDB , Microsoft SQL Server , Oracle , Sybase , SAP HANA , MemSQL , SQLite and IBM DB2 . A database is not generally portable across different DBMSs, DBMS can interoperate by using standards such as SQL and ODBC or JDBC to allow a single application to work with more than one DBMS. Database management systems are classified selon Often the database modelThat They support; The most popular database systems since the 1980s have all supported the relational model as represented by the SQL language. [ Disputed – discuss ] Sometimes a DBMS is loosely referred to as a “database”. Database management systems are classified selon Often the database model That They support; The most popular database systems since the 1980s have all supported the relational model as represented by the SQL language. [ Disputed – discuss ] Sometimes a DBMS is loosely referred to as a “database”. Database management systems are classified selon Often the database model That They support; The most popular database systems since the 1980s have all supported the relational model as represented by the SQL language. [ Disputed – discuss ]Sometimes a DBMS is loosely referred to as a “database”.
Terminology and overview
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) consistant en 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 to manipulate 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 with the use of a database management system. 
Existing DBMSs provide the following functions:
- Data definition – Creation, modification and removal of definitions.
- Update – Insert, modify, and delete the current 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 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. 
Both DBMS and 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. Database servers are usually multiprocessor computers, with generous memory and RAID disk arrays used for stable storage. RAID is used for recovery of data if any of the disks fail. 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,
Since DBMSs included a significant market , computer and storage vendors often take into account DBMS requirements in their own development plans. 
Databases and DBMSs can be categorized selon the database model (s) That They support (Such As relational XML gold), the kind (s) of computer They Run one (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.
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 of database applications include computerized library systems, flight booking systems , computerized inventory systems parts , And Many content management systems That store websites have collections of webpages in a database.
General-purpose and special-purpose DBMSs
DBMS may become a complex software system and its development effort. [A] Some general-purpose DBMSs such as Adabas , Oracle and DB2 have been upgraded since the 1970s. General-purpose DBMSs to meet the needs of as many applications as possible, which adds to the complexity. However, since their development cost can be spread over a large number of users, they are often the most cost-effective approach. On the other hand, a general-purpose DBMS may introduce unnecessary overhead. Therefore, many systems use a special-purpose DBMS. A common example is an email system That Performs Many of the functions of a general-purpose DBMS Such As the insertion and deletion of messages Composed of various items of data or messages Associating With A Particular email address; DBMS is a DBMS application that is designed to provide a wide range of DBMS services.
Application software can access a database Often On Behalf Of end-users, without Exposing the DBMS Directly interface. Application programers may use a wire protocol directly, or more likely through an application programming interface . Database designers and database administrators interact with the DBMS and the DBMSs’ external interfaces and tuning parameters.
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 two hand early navigational data models Were the hierarchical model , epitomized by IBM’s IMS system, and the CODASYL model ( network model ) Implemented in a number of products Such As IDMS .
The relational model , first proposed in 1970 by Edgar F. Codd , departed from this tradition by insisting that applications should be searched 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 wide-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, 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 NewSQLdatabases attempted new implementations that retained the relational / SQL model while aiming to match the high performance of the DBMSs.
1960s, navigational DBMS
The introduction of the term database coincided with the availability of direct-access storage 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 a “data-base” in a specific technical sense.
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 establishment and standardization of COBOL . In 1971, the Database Task Group released their standard, which was known as the “CODASYL approach”.
The CODASYL approach is based on the “manual” of a large network. Applications could find records by one of three methods:
- Use of a primary key (known as a CALC key, typically implemented by hashing )
- Navigating relationships (called sets) from one record to another
- Scanning all the records in a sequential order
Later systems added B-trees to provide alternate access paths. Many CODASYL databases also added a very straightforward query language. However, in the final tally, CODASYL was very complex and required significant training and effort to produce useful applications.
IBM also had their 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 . CODASYL’s network model. CODASYL’s network model. Bachman’s 1973 Turing Award presentation was The Programmer as Navigator . IMS is classified [ by whom? ] As a hierarchical database . IDMS and Cincom Systems ‘ TOTAL database are classified as network databases. IMS remains in use as of 2014 . 
1970s, relational DBMS
Edgar Codd Worked at IBM in San Jose, California , in One of Their offshoot offices That Was 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 . 
In this paper, he has a new system for storing and working with large databases. Instead of records being white Stored In Some sort of linked list of free-form records as in CODASYL, Codd’s idea to use Was a ” table ” of fixed-length records, With Each table used for a different type of entity. A linked-list system would be very inefficient when storing “sparse” databases where any of the data could be left empty. The relational model solved this by splitting the data into a series of normalized tables (or relationships ), with optional Elements being white Moved out of the hand table to Where They Would take up room only if needed. Data may be freely inserted, deleted and edited in these tables,
The relational model also allows the content of the database to evolve without constant rewriting of links and pointers. The relational part of the relationship between the two entities is the same as that of a traditional relationship. Thus, a relational model can express both hierarchical and navigational models, as well as its native tabular model, allowing for pure or combined modeling in terms of these three models, as the application requires.
For instance, the use of information about the users, their names, their names, their names and their names. In the navigational approach, all of this data would be placed in a single record, and unused items would simply not be placed in the database. In the relational approach, the data would be normalized into a table, for an instance. Records would have been provided.
Linking the information back together is the key to this system. In the relational model, a bit of information was used as a ” key “, uniquely defining a particular record. When was the last time we were in this hotel? For instance, if the login name of a user is unique, This simple “re-linking” of related data back into a single collection is something that traditional computer languages are not designed for.
Just as the navigational approach would require programs to loop in order to collect records, the relational approach would require loops to collect information about any onerecord. Codd’s suggestions were a set-oriented language, which would later spawn the ubiquitous SQL . Using a branch of mathematics as a tuple calculus , it is possible to perform a set of operations in a single operation.
Codd’s paper was picked up by two people at Berkeley, Eugene Wong and Michael Stonebraker . They initiated a project known as INGRES, which had already been allocated for a project and student program to produce code. Beginning in 1973 Ingres Delivered icts first test products qui Were Generally ready for use in 1979. Widespread Ingres Was similar to System R in a number of ways, Including the use of a “language” for data access , Known As IS . Over time, INGRES moved to the emerging SQL standard.
IBM itself has a 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 developing the MICRO Information Management System  based on DL Childs’ Set-Theoretic Data model.    MICRO was used to manage very large data sets by the US Department of Labor , the US Environmental Protection Agency , and the University of Alberta , University of Michigan , and Wayne State University . IBM mainframe computers using the Michigan Terminal System .  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 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 unsuccessful because they 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 some applications for Pursued By Some companies like Netezza and Oracle ( Exadata ).
Late 1970s, SQL DBMS
IBM started working on a prototype system based on Codd’s concepts as System R in the early 1970s. The first version was ready in 1974/5, and then work on a multi-table system in which the data could be split so that all of the data for a record Single wide “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. CODASYL, pushing IBM to develop a real production version of System R, known as SQL / DS , and later, Database 2 (DB2).
Larry Ellison ‘s Oracle started on a different chain, based on IBM’ s papers on System R, and beat IBM to market when the first version was released in 1978. [ citation needed ]
PostgreSQL: PostgreSQL: PostgreSQL – PostgreSQL – PostgreSQL – PostgreSQL – PostgreSQL – PostgreSQL PostgreSQL is often used for global mission critical applications (the .org and .info domain names registrars use their large data stores , as well as large institutions and financial institutions).
In Sweden, Codd’s paper was also read and Mimer SQL was developed from the mid-1970s at Uppsala University . In 1984, this project was consolidated into an independent enterprise. In the early 1980s, Mimer introduced transaction handling for high robustness in applications, an idea that was subsequently implemented on most other DBMSs.
Another data model, the entity-relationship model , emerged in 1976 and gained popularity for database design . Later on, the entity-relationship constructs were retrofitted as a data modeling construct for the relational model, and the difference between the two have become irrelevant. [ Citation needed ]
1980s, on the desktop
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 to use. 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. , So the user can concentrate on what he is doing, rather than having the dirty details of opening, reading, and closing files, and managing space allocation. ”
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 process data in their databases as objects. This is a translation from the original French version of this article. This allows for relationships between data to be related to objects and their attributes and not to individual fields.  The term ” object-relational impedance mismatch ” describes the inconvenience of translating between programmed objects and database tables. Object databases, and object-relational databases. This is an attempt to solve this problem by providing an object-oriented language. On the programming side, libraries known as object-relational mappings (ORMs) attempt to solve the same problem.
2000s, NoSQL and NewSQL
XML databases are a type of structured document-oriented database that allows querying based on XML document attributes. XML databases are mostly used in enterprise database management , where XML is being used as the machine-to-machine data interoperability standard. XML database management systems include financial software MarkLogic and Oracle Berkeley DB XML , and has free use software Clusterpoint Distributed XML / JSON Database . All are enterprise software database platforms and industry standard medium ACID -compliant transaction processing with strong database consistency characteristics and high level of database security.
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 . Reviews The most popular NoSQL systems include MongoDB , Couchbase , Riak , Memcached , Redis , CouchDB , Hazelcast , Apache Cassandra , and HBase ,  qui are all open source software products.
In recent years, There Was a high demand for massively distributed databases with high tolerance score goal selon the CAP theorem it is not possible 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 the 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 provide the same scalable performance of a transactional processing (read-write) workloads while still using SQL and maintaining the ACID guarantees of a traditional database system. Such databases include Google F1 / Spanner , Citus , CockroachDB , TiDB , ScaleBase , MemSQL , NuoDB ,  and VoltDB .
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, and related concurrency control techniques, query languages and query optimization methods, RAID , and more.
The database research area dedicated HAS Several academic journals (for example, ACM Transactions on Database Systems -TODS, Data and Knowledge Engineering -DKE) and annual conferences (eg, ACM SIGMOD ACM PODS , VLDB , IEEE ICDE).
One way to classify databases involves the type of their contents, for example: bibliographic , document-text, statistical, or multimedia objects. Another way is by way of 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.
- An in-memory database is a database That Resides In Primarily hand memory , goal Typically is backed-up by non-volatile computer data storage. Main memory databases are faster than disk databases, and so are often used.  SAP HANA is a very hot topic for in-memory database. By May 2012, HANA was able to run on servers with 100TB main memory powered by IBM. The biggest SAP customers.
- An active database includes an event-driven architecture which can respond to conditions both inside and outside the database. Possible uses include security monitoring, alerting, statistics gathering and authorization. Many databases provide active database functions in the form of database triggers .
- A cloud database relies on cloud technology . Both the database and MOST icts of DBMS resides remotely, “in the cloud”, while icts Both applications are developed by programmers and later maintained and Utilized by (app’s) end-users through a web browser and open APIs .
- Data warehouses archive data from operational databases and often from external sources The warehouse becomes the central source of data for use by managers and other end-users who may not have access to operational data. For example, sales data might be aggregated to weekly totals and converted from internal product codes to use UPCs so That They Can Be Compared with ACNielsen data. Some basic and essential components of data warehousing include extracting, analyzing, and mining data, transforming, loading, and managing data.
- A deductive database combines logic programming with a relational database, for example by using the Datalog language.
- A distributed database is one in qui Both the data and the DBMS span multiple computers.
- A document-oriented database is designed for storing, retrieving, and managing document-oriented, or semi structured data, information. Document-oriented databases are one of the main categories of NoSQL databases.
- An embedded database system is a DBMS that is tightly integrated with an application software that requires access to stored data in such a way that the DBMS is hidden from the application’s end-users and requires little or no maintenance. 
- End-user databases consist of data developed by individual end-users. Examples of these are collections of documents, spreadsheets, presentations, multimedia, and other files. Several products exist to support such databases. Some of them are much simpler than full-fledged DBMSs, with more elementary DBMS functionality.
- A federated database system includes several distinct databases, each with its own DBMS. It is handled as a single database by a federated database management system (FDBMS), qui Transparently integrals multiple autonomous DBMSs, Possibly of different kinds (in qui box It Would aussi be a heterogeneous database system ), and Provides Them with an integrated conceptual view .
- Sometimes the term multi-database is used as a synonym for federated database, though it may refer to a less integrated (eg, without an FDBMS and a managed integrated schema) group of databases that cooperate in a single application. In this case, typically middleware is used for distribution, which typically includes an atomic commit protocol (ACP), eg, the two-phase commit protocol , to allow distributed (global) transactions across the participating databases.
- A graph database is a kind of database that uses graphs with nodes, edges, and properties to represent and store information. General graph databases that can store any graph that is distinct from specialized graph databases such as triplestores and network databases .
- An array DBMS is a kind of DBMS that allows to model, store, and retrieve (usually large) multi-dimensional arrays such as satellite images and climate simulation output.
- In a hypertext or hypermedia database, any word or a piece of text representing an object, eg, another piece of text, an article, a picture, or a film, can be hyperlinked to that object. Hypertext databases are particularly useful for organizing large quantities of disparate information. For example, they are useful for organizing online encyclopedias , where users can conveniently jump around the text. The World Wide Web is a large distributed hypertext database.
- A knowledge base (abbreviated KB , kb or Δ   ) is a special kind of database for knowledge management , providing the means for the computerized collection, organization, and retrieval of knowledge . Also a collection of data representing problems with their solutions and related experiences.
- A mobile database can be carried on or synchronized from a mobile computing device.
- Operational databases store detailed data about the operations of an organization. They typically process relatively high volumes of updates using transactions . Examples include customer databases that record information about employees, companies , data inventories, and other information. Financial databases that keep track of the organization’s money, accounting and financial dealings.
- A parallel database seeks to Improve performance through parallelization for tasks Such as loading data, building Evaluating indexes and queries.
- The major parallel DBMS architectures which are induced by the underlying hardware architecture are:
- Shared memory architecture , where multiple processors share the main memory space.
- Shared disk architecture , where each processing unit has its own main memory, but all units share the other storage.
- Shared nothing architecture , where each processing unit has its own memory and other storage.
- The major parallel DBMS architectures which are induced by the underlying hardware architecture are:
- Probabilistic databases employ fuzzy logic to draw inferences from imprecise data.
- Real-time databases process transactions fast enough for the result to come back and be acted on right away.
- A spatial database can store the data with multidimensional features. The queries on such data include location-based queries, like “Where is the closest hotel in my area?”.
- A temporal database has built-in time aspects for a temporal data model and a temporal version of SQL. More specifically the temporal aspects usually include valid-time and transaction-time.
- A terminology-oriented database builds on an object-oriented database , often customized for a specific field.
- An unstructured data database is intended to store in a manageable and protected way various objects that do not fit naturally and conveniently in common databases. It may include email messages, documents, journals, multimedia objects, etc. The name may be misleading since some objects can be highly structured. However, the entire possible object collection does not fit into a predefined structured framework. Most established DBMSs now support unstructured data in various ways, and newly dedicated DBMSs are emerging.
Design and modeling
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 help 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 ”
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 to hold the data.
Having Produced a conceptual data model That users are happy with, the next course is to translate this into a scheme That implements the relevant data structures dans le database. This process is often called logical database design, and the output is a logical data model in the form of a schema. The DBMS model is based on the DBMS model, which is based on the DBMS model. (The terms data model and database model are often used interchangeably, in this article we use data model for the design of a specific database,
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 using a method is known as normalization . The goal of standardization 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. This is often called physical database design . A key goal during this stage is data independence , meaning that the decisions made for performance optimization should be invisible to end-users and applications. There are two types of data independence: physical independence and logical data independence. Physical design is driven 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 to the database and methods for the data itself.
A database model is a kind of data model That determined the logical structure of a database and determined in Fundamentally qui Manner dataCan Be Stored, Organized, and manipulated. The most popular example of a database model is the relational model, which uses a table-based format.
Common logical data models for databases include:
- Navigational databases
- Hierarchical database model
- Network model
- Graph database
- Relational model
- Entity-relationship model
- Enhanced entity-relationship model
- Object model
- Document model
- Entity-attribute-value model
- Star schema
An object-relational database combines the two related structures.
Physical data models include:
- Inverted index
- Flat file
Other models include:
- Associated model
- Multidimensional model
- Array model
- Multivalue model
Specialized models are optimized for particular types of data:
- XML database
- Semantic model
- Event store
- Time series model
External, conceptual, and internal views
A database management system provides three views of the database data:
- The external level defines how each group of end-users sees the organization of data in the database. A single database can have any number of views at the external level.
- The conceptual level unifies the various external views into a compatible global view.  It provides the synthesis of all the external views. It is out of the scope of the various database end-users, and is rather of interest to database application developers and database administrators.
- The internal level (or physical level ) is the internal organization of data inside a DBMS. It is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the data, using storage facilities Such As indices to Enhance performance. Occasionally it stores data of individual views ( materialized views ), computed from generic data, if performance justification exists for such redundancy. It balances all the external views’ performance requirements, possibly conflicting, in an attempt to optimize overall performance across all activities.
While there is only one conceptual (or logical) and physical (or internal) view of the data, there can be any number of different external views. This article is a preview generated by EVS For example, a financial department of a company needs the payment details of all employees as share of the company’s expenses, profit does not need details about employees That Are The interest of the human resources department. Thus different departments need different views of the company’s database.
The three-level database architecture of the concept of data independence which was one of the major driving forces of the relational model. The idea is that at a higher level. For example, changes in the internal level of the 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 other hand, it is possible to obtain a detailed description of how the data is stored or managed (internal level). In principle every level, and even every external view, can be presented by a different data model. DBMS uses the same data model for both the external and the conceptual levels (eg, 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.
Separating the external , conceptual and internal levels was a major feature of the relational database model implementations that dominate 21st century databases. 
Database languages are special-purpose languages, which do or more of the following:
- Data definition language – defines data types such as creating, altering, or dropping
- Data manipulation language – performs tasks such as inserting, updating, or deleting data occurrences
- Query language – Searching for information and computing
Database languages are specific to a particular model.Notable examples include:
- 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). The American Standards Institute (ANSI) was established in 1986, and the International Organization for Standardization (ISO) was established in 1987. The standards have been consistently enhanced and supported by all mainstream commercial Relational DBMSs.  
- 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 aussi by in-memory XML processors Such As Saxon .
- SQL / XML combines XQuery with SQL. 
A database language may also incorporate features like:
- DBMS-specific Configuration and storage engine management
- Computations to modify query results, like counting, summing, averaging, sorting, grouping, and cross-referencing
- Constraint enforcement (eg in an automotive database,
- Application programming interface for programmer convenience
Performance, security, and availability
Because of the critical importance of a database, it is essential to ensure that the database is accessible to all users.
This paper is based on the study of the physical storage of a database. It includes the internal (physical) level in the database architecture. It also contains all the information needed (eg, metadata , “data about the data”, and internal data structures ) to reconstruct the conceptual level and external level from the internal level when needed. Putting data into storage repository is the responsibility of the database engine aka “storage engine”. Though Typically accessed by a DBMS through the Underlying operating system (and Often Utilizing the operating systems’ file systems as intermediates for storage layout) DBMS and DBMS, and are closely related to the database. A DBMS, while in operation, always has its database residing in several types of storage (eg, memory and external storage). The data base and the additional information are very coded into bits. Data typically resides in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that try to optimize (the best possible) (Eg, when querying the database). And thus are maintained by database administrators. A DBMS, while in operation, always has its database residing in several types of storage (eg, memory and external storage). The data base and the additional information are very coded into bits. Data typically resides in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that try to optimize (the best possible) (Eg, when querying the database). And thus are maintained by database administrators. A DBMS, while in operation, always has its database residing in several types of storage (eg, memory and external storage). The data base and the additional information are very coded into bits. Data typically resides in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that try to optimize (the best possible) (Eg, when querying the database). Memory and external storage). The data base and the additional information are very coded into bits. Data typically resides in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that try to optimize (the best possible) (Eg, when querying the database). Memory and external storage). The data base and the additional information are very coded into bits. Data typically resides in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that try to optimize (the best possible) (Eg, when querying the database).
Some DBMSs support specifying which encoding was used to store data, so multiple encodings can be used in the same database.
Various low-level data storage systems are used by the storage engineer 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 used to increase performance. A common example is Storing materialized views , qui Consist of frequently needed external views or query results. Storing such views saves the expensive computing of them each time they are needed. The downsides of materialized views are in the overhead incurred when updating them to keep them synchronized with their original redundancy data.
This article is a summary of the findings of this study. The results of the present study are summarized in the following table. A distributed database). Updates of a replicated object. In many cases, the entire database is replicated.
Database security deals with all the aspects of protecting the database content, its owners, and its users. It ranges from protection to unintentional data accesses by unauthorized entities (eg, a person or a computer program).
Database access control deals with controlling who is allowed to access what information in the database. The information may include specific database objects (eg, record types, specific records, data structures), certain computations over certain objects (eg, query types, or specific queries) Or other data structures. DBMS interfaces. DBMS interfaces. 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 entities. 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 may have access to the data on an individual employee. If the DBMS provides a way to interactively enter and update the database, this capability allows for managing personal databases.
Data security in general deals with protecting specific data chunks, both physically (ie, from corruption, or destruction, or removal; eg, see physical security ), or the interpretation of them, Looking at the strings of bits that they include, concluding specific valid credit-card numbers, eg, see data encryption ).
Change and access logging records, which was changed, and when it was changed. Logging services. For a forensic database . Sometimes application-level code is used to record changes rather than leaving this to the database. Monitoring can be set up to detect security breaches.
Transactions and concurrency
Database transactions can be used to introduce some degree of fault tolerance and data integrity after recovery from a crash . A database transaction is typically encapsulating a number of operations over a database (eg, reading a database object, writing, acquiring lock , etc.), an abstraction supported in database and also other systems. Each transaction has a defined boundaries in terms of which program / code executes are included in that transaction.
The acronym ACID describes some of the basic properties of a database: Atomicity , Consistency , Isolation , and Durability .
DBMS is not portable to another DBMS (ie, the other DBMS can not run it). However, in some situations, it is desirable to move, migrate a database from one DBMS to another. The Reasons are Primarily economy (different DBMSs May-have different total costs of ownership or TCOs), functional, and operational (different DBMSs May-have different capabilities). The migration involves the DBMS type to another. The transformation should maintain (if possible) the database related application (ie, all related application programs) intact. Thus, the database design and external architecture should be maintained in the transformation. It may also be necessary to have some aspects of the internal architecture. A complex or large database migration may be complicated and costly (one-time) project by itself, which should be factored into the decision to migrate. DBMSs. DBMSs and DBMSs. Typically, a DBMS vendor provides tools to help importing databases from other popular DBMSs.
Building, maintaining, and tuning
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 Utilized for this purpose. A DBMS provides the necessary user interfaces to be used by the database administrators to define the required data structures within the DBMS’s respective data model. DBMS parameters (like security related, storage allocation parameters, etc.).
When the database is ready, 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 data is accumulated during its operation.
After the database is created, initialized and populated it needs to be maintained. Various database parameters may need to be tuned ( tuning ) for better performance; Application’s data structures may be changed or added, new related application programs may be added to the application’s functionality, etc.
Backup and restore
Sometimes it is necessary to bring back a previous state (for many reasons, eg, 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 backupoperation is done occasionally or continuously, where each desired database state (ie, the values of its data and their embedding in database’s data structures) is kept within backup files. When this state is needed, ie, when it is decided by a database administrator to bring the database back to this state, these files are utilized 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.  The semantics of query languages can be tuned according to suitable abstractions of the concrete domain of data. The abstraction of relational database has many interesting applications, in particular, for security purposes, such as fine grained access control, watermarking, etc.
Other DBMS features might include:
- Database logs
- Graphs and graphs for charts and charts, especially in a data warehouse system
- Query optimizer – Performs query optimization is every query to choose an efficient query level (a partial order (tree) of operations) to be Executed to compute the query result. May be specific to a particular storage engine.
- DBMS and DBMS (Database DBMS) and related database, mapping (especially for database) Distributed DBMS), storage allocation and database layout monitoring, storage migration, etc.
- 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, ” DevOps for database”. 
- Comparison of database tools
- Comparison of object database management systems
- Comparison of object-relational database management systems
- Comparison of relational database management systems
- Data hierarchy
- Data bank
- Data store
- Database theory
- Database testing
- Database-centric architecture
- Journal of Database Management
- Question-focused dataset
- Jump up^ This article quotes a development time of 5 years involving 750 people for DB2 release 9 alone (Chong et al., 2007)
- Jump up^ “Database – Definition of database by Merriam-Webster” . Merriam-webster.com .
- Jump up^ Ullman & Widom 1997, p. 1.
- Jump up^ “Update – Definition of update by Merriam-Webster” . Merriam-webster.com .
- Jump up^ “Retrieval – Definition of retrieval by Merriam-Webster” . Merriam-webster.com .
- Jump up^ “Administration – Definition of administration by Merriam-Webster” . Merriam-webster.com .
- Jump up^ Tsitchizris & Lochovsky 1982.
- Jump up^ Beynon-Davies 2003.
- Jump up^ Nelson & Nelson 2001.
- Jump up^ Bachman 1973.
- Jump up^ “TOPDB Top Database index” . Pypl.github.io .
- Jump up^ “database, n” . OED Online . Oxford University Press. June 2013 . Retrieved July 12, 2013 .
- Jump up^ IBM Corporation. “IBM Information Management System (IMS) 13 Transaction and Database Servers delivers high performance and low total cost of ownership . ” Retrieved Feb 20, 2014 .
- Jump up^ Codd 1970.
- Jump up^ Hershey & Easthope 1972.
- Jump up^ North 2010.
- Jump up^ Childs 1968a.
- Jump up^ Childs 1968b.
- Jump up^ MICRO Information Management System (Version 5.0) Reference Manual , MA Kahn Rumelhart DL, and BL Bronson, October 1977 Institute of Labor and Industrial Relations (ILIR), University of Michigan and Wayne State University
- Jump up^ Interview with Wayne Ratliff. The FoxPro History. Retrieved on 2013-07-12.
- Jump up^ Development of an object-oriented DBMS; Portland, Oregon, United States; Pages: 472-482; 1986; ISBN 0-89791-204-7
- Jump up^ “Oracle Berkeley DB XML” (PDF) . Retrieved 10 March 2015 .
- Jump up^ “ACID Transactions, MarkLogic” . Retrieved 10 March 2015 .
- Jump up^ “Clusterpoint Database at a Glance” . Archived from the original on 2 April 2015 . Retrieved 10 March 2015 .
- Jump up^ “DB-Engines Ranking” . January 2013 . Retrieved 22 January 2013 .
- Jump up^ Proctor 2013.
- Jump up^ “TeleCommunication Systems Signs up as a Reseller of TimesTen Mobile Operators and Gain Real-Time Platforms for Location-Based Services” . Business Wire . 2002-06-24. [ Dead link ]
- Jump up^ Graves, Steve. “COTS Databases for Embedded Systems”,Embedded Computing Designmagazine, January 2007. Retrieved on August 13, 2008.
- Jump up^ Argumentation in Artificial Intelligence by Iyad Rahwan, Guillermo R. Simari
- Jump up^ “OWL DL Semantics” . Retrieved 10 December 2010 .
- Jump up^ itl.nist.gov (1993) Integration Definition for Information Modeling (IDEFIX) . 21 December 1993.
- ^ Jump up to:a b Date 2003 , pp. 31-32.
- Jump up^ Chapple 2005.
- Jump up^ “Structured Query Language (SQL)” . International Business Machines. October 27, 2006 . Retrieved 2007-06-10 .
- Jump up^ Wagner 2010.
- Jump up^ Halder & Cortesi 2011.
- Jump up^ Ben Linders (January 28, 2016). “How Database Administration Fits into DevOps” . Retrieved April 15, 2017 .