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History of DBMS

Last Updated : 26 Feb, 2024
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Data is a collection of facts and figures. The data collection was increasing day to day and they needed to be stored in a device or a safer software. Charles Bachman was the first person to develop the Integrated Data Store (IDS) which was based on a network data model for which he was inaugurated with the Turing Award (The most prestigious award which is equivalent to the Nobel prize in the field of Computer Science.). It was developed in the early 1960s. In the late 1960s, IBM (International Business Machines Corporation) developed the Integrated Management Systems which is the standard database system used to date in many places. It was developed based on the hierarchical database model. It was during the year 1970 that the

relational database model

was developed by Edgar Codd. Many of the database models we use today are relational-based. It was considered the standardized database model from then. The relational model was still in use by many people in the market. Later during the same decade (1980’s), IBM developed the

Structured Query Language (SQL)

as a part of the R project. It was declared as a standard language for the queries by ISO and ANSI. The Transaction Management System for processing transactions was also developed by James Gray for which he was felicitated the Turing Award. Further, there were many other models with rich features like complex queries, datatypes to insert images, and many others. The Internet Age has perhaps influenced the data models much more. Data models were developed using object-oriented programming features, embedding with scripting languages like

Hyper Text Markup Language (HTML)

for queries. With humongous data being available online, DBMS is gaining more significance day by day.

Techniques for data storage and processing have evolved over the years:

  • 1950s and early 1960s: Magnetic tapes were developed for data storage. Data processing tasks such as payroll were automated, with data stored on tapes. Processing of data consisted of reading data from one or more tapes and writing data to a new tape. Data could also be input from punched card decks, and output to printers. For example, salary raises were processed by entering the raises on punched cards and reading the punched card deck in synchronization with a tape containing the master salary details. The records had to be in the same sorted order. The salary raises would be added to the salary read from the master tape, and written to a new tape; the new tape would become the new master tape. Tapes (and card decks) could be read only sequentially, and data sizes were much larger than main memory; thus, data processing programs were forced to process data in a particular order, by reading and merging data from tapes and card decks.
  • Late 1960s and 1970s: Widespread use of hard disks in the late 1960s changed the scenario for data processing greatly, since hard disks allowed direct access to data. The position of data on disk was immaterial, since any location on disk could be accessed in just tens of milliseconds. Data were thus freed from the tyranny of sequentiality. With disks, network and hierarchical databases could be created that allowed data structures such as lists and trees to be stored on disk. Programmers could construct and manipulate these data structures. A landmark paper by Codd [1970] defined the relational model and nonprocedural ways of querying data in the relational model, and relational databases were born. The simplicity of the relational model and the possibility of hiding implementation details completely from the programmer were enticing indeed. Codd later won the prestigious Association of Computing Machinery Turing Award for his work.
  • 1980s: Although academically interesting, the relational model was not used in practice initially, because of its perceived performance disadvantages; relational databases could not match the performance of existing network and hierarchical databases. That changed with System R, a groundbreaking project at IBM Research that developed techniques for the construction of an efficient relational database system. Excellent overviews of System R are provided by Astrahan et al. [1976] and Chamberlin et al. [1981]. The fully functional System R prototype led to IBM’s first relational database product, SQL/DS. At the same time, the Ingres system was being developed at the University of California at Berkeley. It led to a commercial product of the same name. Initial commercial relational database systems, such as IBM DB2, Oracle, Ingres, and DEC Rdb, played a major role in advancing techniques for efficient processing of declarative queries. By the early 1980s, relational databases had become competitive with network and hierarchical database systems even in the area of performance. Relational databases were so easy to use that they eventually replaced network and hierarchical databases; programmers using such databases were forced to deal with many low-level implementation details, and had to code their queries in a procedural fashion. Most importantly, they had to keep efficiency in mind when designing their programs, which involved a lot of effort. In contrast, in a relational database, almost all these low-level tasks are carried out automatically by the database, leaving the programmer free to work at a logical level. Since attaining dominance in the 1980s, the relational model has reigned supreme among data models. The 1980s also saw much research on parallel and distributed databases, as well as initial work on object-oriented databases.
  • Early 1990s: The SQL language was designed primarily for decision support applications, which are query-intensive, yet the mainstay of databases in the 1980s was transaction-processing applications, which are update-intensive. Decision support and querying re-emerged as a major application area for databases. Tools for analyzing large amounts of data saw large growths in usage. Many database vendors introduced parallel database products in this period. Database vendors also began to add object-relational support to their databases.
  • 1990s: The major event of the 1990s was the explosive growth of the World Wide Web. Databases were deployed much more extensively than ever before. Database systems now had to support very high transaction-processing rates, as well as very high reliability and 24 × 7 availability (availability 24 hours a day, 7 days a week, meaning no downtime for scheduled maintenance activities). Database systems also had to support Web interfaces to data.
  • 2000s: The first half of the 2000s saw the emerging of XML and the associated query language XQuery as a new database technology. Although XML is widely used for data exchange, as well as for storing certain complex data types, relational databases still form the core of a vast majority of large-scale database applications. In this time period we have also witnessed the growth in “autonomic-computing/auto-admin” techniques for minimizing system administration effort. This period also saw a significant growth in use of open-source database systems, particularly PostgreSQL and MySQL. The latter part of the decade has seen growth in specialized databases for data analysis, in particular column-stores, which in effect store each column of a table as a separate array, and highly parallel database systems designed for analysis of very large data sets. Several novel distributed data-storage systems have been built to handle the data management requirements of very large Web sites such as Amazon, Facebook, Google, Microsoft and Yahoo!, and some of these are now offered as Web services that can be used by application developers. There has also been substantial work on management and analysis of streaming data, such as stock-market ticker data or computer network monitoring data. Data-mining techniques are now widely deployed; example applications include Web-based product-recommendation systems and automatic placement of relevant advertisements on Web pages.

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