History of DBMS

The first database management systems (DBMS) were created to handle complex data for businesses in the 1960s. These systems included Charles Bachman's Integrated Data Store (IDS) and IBM's Information Management System (IMS). Databases were first organized into tree-like structures using hierarchical and network models.

Edgar F. Codd popularized the relational model in the 1970s, transforming database management systems (DBMS) with the concept of arranging data in tables, or relations and utilizing SQL for queries. As a result, contemporary DBMS systems like Oracle and MySQL were established. These systems are still developing today, incorporating newer technologies like NoSQL databases to handle unstructured data.

What is DBMS?

A Database Management System (DBMS) is a Software system designed to handle, store, retrieve, and work with data in databases. It ensures effective data management by serving as an interface between databases and end users. A DBMS gives users/developers the ability to create, edit, and remove databases as well as organize the environment in which data is kept in tables, records, and fields.

DBMS supports multiple operations, including data storage, retrieval, updating, and database management. Usually, DBMS communicates with the database using Structured Query Language (SQL). A database management system (DBMS) is primarily used to manage large amounts of data in an organized manner while maintaining data security, consistency, and integrity.

Two important characteristics of DBMSs are data independence, which guarantees that modifications to data structures do not impact application layers, and data abstraction, which makes database interactions simpler by shielding users from the complexity of data storage. Additionally, it provides concurrency control, which permits several users to access data concurrently and peacefully

Evolution of Data Storage and Processing Techniques

• 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.

• 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. 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.

• 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.

• 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.

Conclusion

The development of database management systems (DBMS) has revolutionized the organization, storage, and retrieval of data. From the first network and hierarchical models to Edgar F. Codd's ground-breaking relational model, database management systems (DBMS) have grown to be essential to the management of structured data. The use of Structure Query Language (SQL) in various industries was further increased by its ascent to become a standard language for databases. NoSQL and other contemporary database systems are paving the way for managing unstructured data today with the rise of big data, demonstrating the ongoing innovation in the data management space.