A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modeling techniques.
A Database Management System allows a person to organize, store, and retrieve data from a computer. It is a way of communicating with a computer’s “stored memory.” In the very early years of computers, “punch cards” were used for input, output, and data storage. Punch cards offered a fast way to enter data, and to retrieve it. Herman Hollerith is given credit for adapting the punch cards used for weaving looms to act as the memory for a mechanical tabulating machine, in 1890. Much later, databases came along.
Databases (or DBs) have played a very important part in the recent evolution of computers. The first computer programs were developed in the early 1950s, and focused almost completely on coding languages and algorithms. At the time, computers were basically giant calculators and data (names, phone numbers) was considered the leftovers of processing information. Computers were just starting to become commercially available, and when business people started using them for real-world purposes, this leftover data suddenly became important.
Enter the Database Management System (DBMS). A database, as a collection of information, can be organized so a Database Management System can access and pull specific information. In 1960, Charles W. Bachman designed the Integrated Database System, the “first” DBMS. IBM, not wanting to be left out, created a database system of their own, known as IMS. Both database systems are described as the forerunners of navigational databases.
By the mid-1960s, as computers developed speed and flexibility, and started becoming popular, many kinds of general use database systems became available. As a result, customers demanded a standard be developed, in turn leading to Bachman forming the Database Task Group. This group took responsibility for the design and standardization of a language called Common Business Oriented Language (COBOL). The Database Task Group presented this standard in 1971, which also came to be known as the “CODASYL approach.”
Edgar Codd worked for IBM in the development of hard disk systems, and he was not happy with the lack of a search engine in the CODASYL approach, and the IMS model. He wrote a series of papers, in 1970, outlining novel ways to construct databases. His ideas eventually evolved into a paper titled, A Relational Model of Data for Large Shared Data Banks, which described new method for storing data and processing large databases. Records would not be stored in a free-form list of linked records, as in CODASYL navigational model, but instead used a “table with fixed-length records.”
IBM had invested heavily in the IMS model, and wasn’t terribly interested in Codd’s ideas. Fortunately, some people who didn’t work for IBM “were” interested. In 1973, Michael Stonebraker and Eugene Wong (both then at UC Berkeley) made the decision to research relational database systems. The project was called INGRES (Interactive Graphics and Retrieval System), and successfully demonstrated a relational model could be efficient and practical. INGRES worked with a query language known as QUEL, in turn, pressuring IBM to develop SQL in 1974, which was more advanced (SQL became ANSI and OSI standards in 1986 1nd 1987). SQL quickly replaced QUEL as the more functional query language.
RDBM Systems were an efficient way to store and process structured data. Then, processing speeds got faster, and “unstructured” data (art, photographs, music, etc.) became much more common place. Unstructured data is both non-relational and schema-less, and Relational Database Management Systems simply were not designed to handle this kind of data.
A database is a collection of information that is organized in tables and stored on a computer system. This information can be updated or modified as required. We can also say it’s like a room in an office which has files in it. If we don’t have a defined process we will not know how to get that data from the room.
Similarly, a database management system (DBMS) is a software for creating and managing data in the databases. The DBMS provides users and programmers a defined process for data retrieval, management, updating, and creation.
Database Management Software also keeps data guarded and safe. These tools help in reducing data redundancy and maintaining the efficiency of data. Some of them are open-source and some are commercial with specific features.
Based on the usage and requirement we can choose a software tool that has needed features and the desired output.
Given below is the list of most popular database management systems:
- SolarWinds Database Performance Analyzer - SolarWinds Database Performance Analyzer has the features of Machine Learning, Cross-Platform Database Support, Expert Tuning Advisors, Cloud Database Support, and Automation Management API, etc.
- Oracle RDBMS - Oracle database is the most widely used object-relational database management software. The latest version of this tool is 12c where c means cloud computing.
- IBM DB2 - It is very easy to install and set up and data is easily accessible, we can save the huge amount of data almost up to pet bytes.
- Altibase - Altibase is an enterprise-grade, high performance, and relational open-source database. Altibase has over 650 enterprise clients including 8 Fortune Global 500 companies and has been deployed over 6,000 mission-critical use cases in various industries.
- Microsoft SQL Server - Developed in the year 1989. The latest updated version came in 2016. The language used is Assembly C, Linux, C++ for writing it. Compatible with Oracle provides efficient management of workload and allows multiple users to use the same database.
- SAP Sybase ASE - ASE stands for Adaptive Server Enterprise. Its latest version is 15.7. It was started in the middle of the eighties. It can perform millions of transactions in a minute, using cloud computing even mobile devices can be synchronized with the database.
- Teradata - Data import and export is easy, multiple processing is possible at the same time, data can be easily distributed, useful for very large databases.
- ADABAS - Data processing speed is fast, irrespective of the load, the output of any transaction is reliable, its architecture is quite flexible and keeps pace with the changing demands.
- MySQL - High-speed data processing, use of triggers increases productivity, with rollback and commit helps in data recovery if required.
- FileMaker - It can be connected across platforms like connections to SQL are possible, information sharing is easier because of the cloud.
- Microsoft Access - It is an affordable database management system used mostly by e-commerce sites.
- Informix - Hardware uses less space, data is available at all times and does not need maintenance time. It is developed by IBM.
- SQLite - It does not need much space hence, it can be used for storing small to medium size websites. It is fast and does not need to set up.
- PostgresSQL - It is an object-relational database. The data remains secure. Data retrieval is faster. Data sharing through dashboards is faster
- Amazon RDS - Setting up and operating is very easy and the database is very secure. The backing up of the database is an inbuilt feature. Recovery of data is also an inbuilt feature managed within.
- MongoDB - It can process a large amount of data simultaneously and uses internal memory so the data is easily accessible, the use of very complex joins is not supported, scaling is easily possible. Queries can be easily optimized for output.
- Redis - The database speed is very good, data types like hashes and strings are also supported and the performance of queries is high.
- CouchDB - Secure system network, efficient error handling, the output is reliable and fast.
- Neo4j - It has a large capacity server, this database stores data in the form of graphs. It is also called a graph database management system.
- OrientDB - It is a graphical database. It is widely used across big data market and in real-time web-based applications.