Database Management System: Difference between revisions

Revision as of 09:06, 6 July 2025

Introduction

Database Management System is a software system that enables the creation, management, and manipulation of databases. A database is a structured collection of data, and the database management system (DBMS) is the intermediary that allows users to interact with this data through various operations like creating, retrieving, updating, and deleting data. DBMSs are essential in today’s information-centric society, establishing a foundation for applications dealing with large volumes of data, from business applications to complex web services.

Database management systems classify and organize data in a way that makes it easy to access and modify. They provide safety and data integrity by ensuring that the data is stored in a manner that minimizes redundancy and maintains consistency. The design of a DBMS allows users to retrieve data efficiently, manage access restrictions, and ensure that data remains valid and secure against unauthorized access.

Background

The origins of database systems date back to the early days of computing in the 1960s and 1970s, when organizations primarily used file systems to store information. As data grew in volume and complexity, the limitations of file systems became apparent. These systems lacked standardized methods to retrieve and manipulate data efficiently, leading to the development of database management systems.

Historical Development

The first generation of database systems was the hierarchical and network models, both developed between the 1960s and 1970s. The hierarchical model, exemplified by systems like the IBM Information Management System (IMS), supported one-to-many relationships. In contrast, the network model allowed for more complex relationships among data sets by enabling many-to-many relationships.

By the late 1970s, the relational database model emerged, largely due to the influence of Edgar F. Codd, a computer scientist at IBM. This model emphasized data representation through tables and introduced the concept of Structured Query Language (SQL) for querying and manipulating data. The popularity of the relational model led to the widespread adoption of SQL as a standard language for database interaction.

Evolution in Technology

With the rise of personal computing in the 1980s and 1990s, database management systems became more accessible to smaller organizations and individual users. Relational databases dominated the market, exemplified by products such as Oracle Database, Microsoft SQL Server, and MySQL. In the early 2000s, the advent of the Internet and web-based applications created demand for new database technologies, leading to the development of NoSQL databases. These systems provide flexibility and scalability, suited for huge volumes of unstructured data.

Today, DBMSs are continually evolving, embracing cloud computing and big data technologies. Administrators can now manage databases remotely using cloud platforms, ensuring efficiency and cost-effectiveness.

Architecture

The architecture of a database management system refers to its overall structure and design, which significantly affects its functionality and performance. Generally, DBMS architecture can be divided into three major levels: the internal level, the conceptual level, and the external level.

Three-Level Architecture

The three-level architecture was proposed by the ANSI/SPARC (American National Standards Institute/Standards Planning and Requirements Committee) to provide a framework for data abstraction. Each level serves a distinct role in database management.

The internal level is the lowest level in the architecture. It details how data is physically stored in the database. This level involves storage structures, access methods, and file organization. Specific implementations may vary based on the hardware and software capacity, yet the internal structure is abstracted away from end-users.

The conceptual level sits above the internal level. It provides a community view of the entire database, defining what data is stored and the relationships among the data. This level allows users and application developers to focus on the design of the database without needing to understand the physical storage details.

The external level is the highest level in the architecture and represents how individual users view the data. This level provides various user interfaces and allows for specific views tailored to meet particular user requirements. By shielding the users from complexities, the external level ensures that it is easier for individuals to interact with the database.

Database Models

Different database models embody varying structures and approaches to data presentation. The most recognized database models include:

The relational model utilizes tables to represent data. The rows correspond to records, while the columns represent fields of data. Relationships between tables are defined using keys.
The hierarchical database model arranges data in a tree-like structure where each record has a single parent with zero or more children.
The network database model, similar to the hierarchical model, allows for multiple parent-child relationships, making it more flexible but also more complex.
The object-oriented database model encapsulates data and behavior in single entities using the principles of object-oriented programming.

With the expansion of applications that manage vast amounts of data, the introduction of NoSQL databases provided an alternative approach, allowing for more flexible schemas and data structures that accommodate various data types.

Implementation

The implementation of a database management system involves several key components and functions that work together to ensure efficient data storage and retrieval. It encompasses not only the software architecture but also system design, deployment, and ongoing management practices.

System Components

A complete database management system typically consists of various components, such as the database engine, tools for database access, and administrative tools. The database engine performs the core functions of storage, retrieval, and management of data, while interfaces facilitate communication between the engine and users or applications.

Database management tools aid in the administration of databases. These tools typically provide functionalities for data backup and recovery, user management, security management, and performance optimization. Also, query optimization tools help improve the efficiency of data retrieval operations by evaluating various query execution plans.

Security and Access Control

Data security is a fundamental aspect of DBMS design. Ensuring the integrity and confidentiality of data requires implementing advanced access control mechanisms. A DBMS may utilize user authentication to grant or restrict access based on user credentials.

Authorization protocols are equally important, determining what actions users can perform on specific pieces of data within the database. Encryption methods can be applied to protect sensitive information both in transit and at rest, and auditing features can track changes made to the database over time.

Data Backup and Recovery

Data loss can severely impact an organization’s operations. Consequently, a robust DBMS must incorporate comprehensive data backup and recovery functionalities. Regular backups prevent data loss due to hardware failures, software bugs, or unforeseen disasters.

Database snapshots, transaction logs, and automated backup scheduling are common practices used to maintain data safety. In case of system failures, recovery procedures enable database administrators to restore lost data to its last consistent state.

Performance Tuning

To achieve optimal performance, database administrators continuously tune the DBMS. This involves assessing and adjusting parameters such as memory allocation, indexing strategies, and query performance. An efficient indexing strategy, for instance, can greatly reduce the time it takes to retrieve data from a large dataset.

Monitoring database performance through various metrics helps avoid bottlenecks and ensures that the database system remains responsive under heavy workloads. Regular maintenance tasks like database reorganization and statistics gathering may also contribute significantly to overall system performance.

Real-world Examples

Numerous industries rely on database management systems to manage their data effectively. The application of DBMS exists across different domains, illustrating the versatility and critical role of these systems.

Financial Institutions

Financial services organizations, including banks and investment firms, leverage database management systems to handle vast amounts of transactional data. These systems manage customer accounts, monitor deposits and withdrawals, and develop analytics for financial forecasting. Robust security measures must be employed to protect sensitive customer data and comply with regulations.

E-commerce Platforms

E-commerce businesses utilize DBMS to maintain product catalogs, manage customer information, process orders, and track inventory. The relational model is commonly used, providing robust relationships between customers and their orders, while NoSQL databases may be employed to handle unstructured data such as customer reviews or social media interactions.

Healthcare Providers

In healthcare, DBMS technology is vital for managing patient records, treatment plans, and billing information. Healthcare providers must ensure the confidentiality of patient data, adhering to regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Data interoperability among different healthcare specialists is facilitated through proper database design.

Telecommunications

Telecommunications companies utilize database management systems to manage extensive customer data, call records, and billing information. They analyze usage patterns for optimizing their services, ensuring efficient customer support, and managing large-scale infrastructure systems.

Criticism and Limitations

Despite their numerous benefits, database management systems face criticism and limitations that can hinder their efficiency and effectiveness.

Complexity and Cost

The implementation and maintenance of a DBMS can be complex and costly. Organizations must invest in skilled personnel for administration and support, often leading to increased operational costs. The learning curve associated with using advanced database features may also deter smaller businesses with limited IT resources.

Performance Bottlenecks

As databases grow in size, performance can degrade without proper maintenance and optimization. Issues such as inefficient queries, lack of indexing, and inadequate hardware can lead to performance bottlenecks. Ensuring continued performance requires persistent monitoring and tuning of the system.

Vendor Lock-in

Organizations that select a specific DBMS often find themselves locked into that vendor’s ecosystem due to proprietary technologies and formats. Migrating to different systems can be resource-intensive and risky, leading to concerns about flexibility and adaptability.

Scalability Issues

While traditional relational databases excel at managing structured data, they may struggle with unstructured or semi-structured data. As a result, some organizations may need to rely on a combination of different database technologies, implementing NoSQL databases for scalability while maintaining relational databases for transactional data.

References

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-== Database Management System ==
-A '''Database Management System''' (DBMS) is a software system that enables the creation, management, and manipulation of databases. It acts as an intermediary between users and databases, allowing for the efficient organization and retrieval of data. DBMS frameworks facilitate the integration of data in various formats, enhance data security, support concurrent access, and ensure integrity through a controlled environment.
 == Introduction ==
+'''Database Management System''' is a software system that enables the creation, management, and manipulation of databases. A database is a structured collection of data, and the database management system (DBMS) is the intermediary that allows users to interact with this data through various operations like creating, retrieving, updating, and deleting data. DBMSs are essential in today’s information-centric society, establishing a foundation for applications dealing with large volumes of data, from business applications to complex web services.
-In the contemporary data-driven landscape, the significance of effective data management cannot be overstated. A DBMS serves as a foundational pillar for businesses, governmental institutions, and numerous organizations by providing the necessary tools to store and manage vast amounts of information efficiently. Through a set of software applications, a DBMS allows users to perform essential operations such as inserting, updating, deleting, and retrieving data while ensuring data integrity and consistency.
+Database management systems classify and organize data in a way that makes it easy to access and modify. They provide safety and data integrity by ensuring that the data is stored in a manner that minimizes redundancy and maintains consistency. The design of a DBMS allows users to retrieve data efficiently, manage access restrictions, and ensure that data remains valid and secure against unauthorized access.
-The evolution of DBMS technologies has led to the emergence of various types of systems, each tailored to meet different application demands. From hierarchical to relational and to more modern NoSQL databases, each generation of DBMS has aimed to improve data handling capabilities to keep pace with the growing volumes and complexity of data.
+== Background ==
+The origins of database systems date back to the early days of computing in the 1960s and 1970s, when organizations primarily used file systems to store information. As data grew in volume and complexity, the limitations of file systems became apparent. These systems lacked standardized methods to retrieve and manipulate data efficiently, leading to the development of database management systems.
-== History ==
+=== Historical Development ===
+The first generation of database systems was the hierarchical and network models, both developed between the 1960s and 1970s. The hierarchical model, exemplified by systems like the IBM Information Management System (IMS), supported one-to-many relationships. In contrast, the network model allowed for more complex relationships among data sets by enabling many-to-many relationships.
-The history of Database Management Systems traces back to the early 1960s with the advent of early computing systems. Before the existence of modern DBMS, data was typically stored in flat files, which were often difficult to manage and manipulate. The first generation of DBMS was based on hierarchical data models, as exemplified by IBM's Information Management System (IMS), released in 1966.
+By the late 1970s, the relational database model emerged, largely due to the influence of Edgar F. Codd, a computer scientist at IBM. This model emphasized data representation through tables and introduced the concept of Structured Query Language (SQL) for querying and manipulating data. The popularity of the relational model led to the widespread adoption of SQL as a standard language for database interaction.
-By the 1970s, the relational model, proposed by Edgar F. Codd, revolutionized the way data could be accessed and managed. Codd's relational model utilized tables (relations) to store data, bringing forth new query languages such as Structured Query Language (SQL), which allowed for more complex data interactions. This innovation laid the groundwork for modern relational database management systems (RDBMS) such as Oracle, Microsoft SQL Server, and MySQL, which began to emerge in the late 20th century.
+=== Evolution in Technology ===
+With the rise of personal computing in the 1980s and 1990s, database management systems became more accessible to smaller organizations and individual users. Relational databases dominated the market, exemplified by products such as Oracle Database, Microsoft SQL Server, and MySQL. In the early 2000s, the advent of the Internet and web-based applications created demand for new database technologies, leading to the development of NoSQL databases. These systems provide flexibility and scalability, suited for huge volumes of unstructured data.
-The 1990s introduced the concept of object-oriented databases, which could store complex data types and support advanced data structures. However, the late 1990s and early 2000s saw another shift toward NoSQL databases, driven by the need for increased scalability and flexibility in handling massive datasets, particularly for web applications. NoSQL systems, such as MongoDB and Cassandra, gained popularity as they could efficiently store unstructured or semi-structured data that does not fit well into a traditional relational model.
+Today, DBMSs are continually evolving, embracing cloud computing and big data technologies. Administrators can now manage databases remotely using cloud platforms, ensuring efficiency and cost-effectiveness.
-== Design and Architecture ==
+== Architecture ==
+The architecture of a database management system refers to its overall structure and design, which significantly affects its functionality and performance. Generally, DBMS architecture can be divided into three major levels: the internal level, the conceptual level, and the external level.
-The architecture of a Database Management System plays a pivotal role in determining its efficiency, scalability, and ease of use. Generally, a DBMS can be categorized into three main levels of architecture, as defined by the ANSI/SPARC (American National Standards Institute/Standards Planning and Requirements Committee) framework:
+=== Three-Level Architecture ===
+The three-level architecture was proposed by the ANSI/SPARC (American National Standards Institute/Standards Planning and Requirements Committee) to provide a framework for data abstraction. Each level serves a distinct role in database management.
-=== 1. External Level ===
+The internal level is the lowest level in the architecture. It details how data is physically stored in the database. This level involves storage structures, access methods, and file organization. Specific implementations may vary based on the hardware and software capacity, yet the internal structure is abstracted away from end-users.
-The external level, also known as the user view level, refers to how data is presented to the users. Different users may require different views of the same database depending on their needs. For example, a sales department may only need access to customer orders, while the finance department may need access to financial transactions and customer data. The external schemas provide customized views and protect user access from the underlying complexities of the database structure.
+The conceptual level sits above the internal level. It provides a community view of the entire database, defining what data is stored and the relationships among the data. This level allows users and application developers to focus on the design of the database without needing to understand the physical storage details.
-=== 2. Conceptual Level ===
+The external level is the highest level in the architecture and represents how individual users view the data. This level provides various user interfaces and allows for specific views tailored to meet particular user requirements. By shielding the users from complexities, the external level ensures that it is easier for individuals to interact with the database.
-Sitting in the middle of the architecture is the conceptual level, which provides a unified view of the entire database. It defines what data is stored in the database and the relationships between the data entities without focusing on how the data is physically stored. The conceptual schema acts as a bridge between the external views and the physical storage of data, enabling data independence and abstraction.
-=== 3. Internal Level ===
-The internal level, or physical level, describes how data is stored physically on storage media. This level describes the data structures (such as B-trees or hash tables) and file storage techniques that the DBMS employs to store data efficiently. Understanding this level is crucial for database administrators who need to optimize the database for performance and storage use.
 === Database Models ===
+Different database models embody varying structures and approaches to data presentation. The most recognized database models include:
+* The relational model utilizes tables to represent data. The rows correspond to records, while the columns represent fields of data. Relationships between tables are defined using keys.
+* The hierarchical database model arranges data in a tree-like structure where each record has a single parent with zero or more children.
+* The network database model, similar to the hierarchical model, allows for multiple parent-child relationships, making it more flexible but also more complex.
+* The object-oriented database model encapsulates data and behavior in single entities using the principles of object-oriented programming.
-Various data models are employed within DBMS architectures to define the structure of data storage. The most prominent models include:
+With the expansion of applications that manage vast amounts of data, the introduction of NoSQL databases provided an alternative approach, allowing for more flexible schemas and data structures that accommodate various data types.
-'''1. Hierarchical Model''': This is the oldest DBMS model organized like a tree structure, with parent-child relationships.
-'''2. Network Model''': This extends the hierarchical model, allowing multiple parent-child relationships.
-'''3. Relational Model''': The most widely used DBMS model, which organizes data into tables and utilizes SQL for data manipulation.
-'''4. Object-oriented Model''': This model integrates object-oriented programming principles, allowing for the storage of complex data types.
-'''5. NoSQL Model''': Designed for unstructured or semi-structured data, this model includes various types such as document, key-value, and graph databases, catering to modern application needs.
-== Usage and Implementation ==
-The implementation of a database management system involves several steps, from needs assessment to system deployment and operation. The typical process includes:
-=== 1. Requirements Analysis ===
-Understanding user requirements is the first and most crucial step in implementing a DBMS. This involves gathering information about the types of data to be stored, the relationships among data, and the types of queries users will need.
-=== 2. Design Phase ===
-Once requirements are analyzed, the database is designed, usually starting with the conceptual schema which defines the entities, attributes, and relationships. Next, the design evolves into a logical schema, which translates the conceptual design into a specific database model, and finally to a physical schema that defines how data will be stored.
-=== 3. Database Creation ===
-Database creation involves a series of steps including setting up the database environment, defining schemas, and constructing relationships. Data normalization is often applied at this stage to minimize redundancy.
-=== 4. Implementation and Testing ===
-The developed database needs to be tested for performance, reliability, and security. This includes evaluating the query responses, ensuring data integrity, and testing backup and recovery processes.
-=== 5. Deployment ===
-Upon successful testing, the DBMS is deployed for public or internal use. Continuous monitoring is essential to evaluate system performance over time to ensure it meets user demands.
-=== 6. Maintenance and Updates ===
-Regular maintenance activities include performance tuning, updates for security vulnerabilities, and data integrity checks. Periodic updates might be necessary to adapt to changing user requirements or technology advancements.
-== Real-world Examples and Comparisons ==
-DBMSs are widely utilized across various sectors and industries. Below are examples of notable database management systems, each serving distinct use cases and requirements.
-=== 1. Oracle Database ===
-Oracle's RDBMS is one of the most popular commercial DBMSs, known for its robustness and scalability. It supports advanced features such as multi-version concurrency control and partitioning, making it suitable for enterprise-level applications.
-=== 2. MySQL ===
-An open-source relational database management system, MySQL is widely used for web-based applications due to its ease of use and integration with various programming languages. MySQL is particularly popular among small to medium-sized businesses.
-=== 3. Microsoft SQL Server ===
-Microsoft SQL Server is another prominent RDBMS, offering tight integration with Microsoft tools and services. It provides various services, including data analysis and reporting tools, making it a preferred choice for many enterprises.
-=== 4. MongoDB ===
-MongoDB is a leading NoSQL database that stores data in flexible JSON-like documents. It enables rapid development and scalability, making it an ideal choice for modern applications requiring quick iterations.
-=== 5. PostgreSQL ===
-PostgreSQL is an open-source RDBMS known for its advanced features and compliance with SQL standards. It supports various data types and offers extensibility options, making it well-suited for complex queries and analytics.
-=== 6. Couchbase ===
+== Implementation ==
+The implementation of a database management system involves several key components and functions that work together to ensure efficient data storage and retrieval. It encompasses not only the software architecture but also system design, deployment, and ongoing management practices.
-A distributed NoSQL DBMS that is designed to provide low-latency access to data. It combines the benefits of document and key-value storage, making it suitable for high-scale applications.
+=== System Components ===
+A complete database management system typically consists of various components, such as the database engine, tools for database access, and administrative tools. The database engine performs the core functions of storage, retrieval, and management of data, while interfaces facilitate communication between the engine and users or applications.
-== Criticism and Controversies ==
+Database management tools aid in the administration of databases. These tools typically provide functionalities for data backup and recovery, user management, security management, and performance optimization. Also, query optimization tools help improve the efficiency of data retrieval operations by evaluating various query execution plans.
-Despite the numerous advantages provided by Database Management Systems, several criticisms and challenges have emerged in association with their use:
+=== Security and Access Control ===
+Data security is a fundamental aspect of DBMS design. Ensuring the integrity and confidentiality of data requires implementing advanced access control mechanisms. A DBMS may utilize user authentication to grant or restrict access based on user credentials.
-=== 1. Complexity ===
+Authorization protocols are equally important, determining what actions users can perform on specific pieces of data within the database. Encryption methods can be applied to protect sensitive information both in transit and at rest, and auditing features can track changes made to the database over time.
-The high complexity of modern DBMS can be a barrier to entry for users unfamiliar with database concepts. The breadth of features and configurations might overwhelm users and require considerable learning investment.
+=== Data Backup and Recovery ===
+Data loss can severely impact an organization’s operations. Consequently, a robust DBMS must incorporate comprehensive data backup and recovery functionalities. Regular backups prevent data loss due to hardware failures, software bugs, or unforeseen disasters.
-=== 2. Cost ===
+Database snapshots, transaction logs, and automated backup scheduling are common practices used to maintain data safety. In case of system failures, recovery procedures enable database administrators to restore lost data to its last consistent state.
-While numerous open-source DBMS options exist, many advanced commercial solutions entail significant licensing and maintenance costs, which can be a financial burden for small businesses.
+=== Performance Tuning ===
+To achieve optimal performance, database administrators continuously tune the DBMS. This involves assessing and adjusting parameters such as memory allocation, indexing strategies, and query performance. An efficient indexing strategy, for instance, can greatly reduce the time it takes to retrieve data from a large dataset.
-=== 3. Vendor Lock-in ===
+Monitoring database performance through various metrics helps avoid bottlenecks and ensures that the database system remains responsive under heavy workloads. Regular maintenance tasks like database reorganization and statistics gathering may also contribute significantly to overall system performance.
-Organizations that choose proprietary DBMS solutions might experience vendor lock-in, wherein migrating to another system becomes complicated and costly due to proprietary data formats or extensive configurations.
+== Real-world Examples ==
+Numerous industries rely on database management systems to manage their data effectively. The application of DBMS exists across different domains, illustrating the versatility and critical role of these systems.
-=== 4. Data Security Concerns ===
+=== Financial Institutions ===
+Financial services organizations, including banks and investment firms, leverage database management systems to handle vast amounts of transactional data. These systems manage customer accounts, monitor deposits and withdrawals, and develop analytics for financial forecasting. Robust security measures must be employed to protect sensitive customer data and comply with regulations.
-DBMS needs robust security features to protect sensitive data. Breaches or vulnerabilities can expose organizations to significant risks, leading to sizable financial and reputational damage.
+=== E-commerce Platforms ===
+E-commerce businesses utilize DBMS to maintain product catalogs, manage customer information, process orders, and track inventory. The relational model is commonly used, providing robust relationships between customers and their orders, while NoSQL databases may be employed to handle unstructured data such as customer reviews or social media interactions.
-=== 5. Performance Issues ===
+=== Healthcare Providers ===
+In healthcare, DBMS technology is vital for managing patient records, treatment plans, and billing information. Healthcare providers must ensure the confidentiality of patient data, adhering to regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Data interoperability among different healthcare specialists is facilitated through proper database design.
-As data volumes increase, DBMS can face performance degradation. Effective tuning and optimization might be required to maintain efficient retrieval and manipulation speeds, which can often be complex tasks.
+=== Telecommunications ===
+Telecommunications companies utilize database management systems to manage extensive customer data, call records, and billing information. They analyze usage patterns for optimizing their services, ensuring efficient customer support, and managing large-scale infrastructure systems.
-== Influence and Impact ==
+== Criticism and Limitations ==
+Despite their numerous benefits, database management systems face criticism and limitations that can hinder their efficiency and effectiveness.
-The advent of Database Management Systems has profoundly influenced technology and society by reshaping how data is stored, managed, and analyzed.
+=== Complexity and Cost ===
+The implementation and maintenance of a DBMS can be complex and costly. Organizations must invest in skilled personnel for administration and support, often leading to increased operational costs. The learning curve associated with using advanced database features may also deter smaller businesses with limited IT resources.
-DBMS technologies underpin many critical applications that power e-commerce, social media, healthcare, and financial systems, leading to unprecedented access to information. Additionally, the development of scalable database solutions has enabled the rise of big data analytics, enabling organizations to derive insights from vast amounts of information and enhance decision-making processes.
+=== Performance Bottlenecks ===
+As databases grow in size, performance can degrade without proper maintenance and optimization. Issues such as inefficient queries, lack of indexing, and inadequate hardware can lead to performance bottlenecks. Ensuring continued performance requires persistent monitoring and tuning of the system.
-In education, DBMS technologies serve as essential tools for teaching database concepts and enabling hands-on experience in data management practices.
+=== Vendor Lock-in ===
+Organizations that select a specific DBMS often find themselves locked into that vendor’s ecosystem due to proprietary technologies and formats. Migrating to different systems can be resource-intensive and risky, leading to concerns about flexibility and adaptability.
-Further advancements in cloud computing have also transformed the landscape of DBMS deployment, allowing organizations to leverage database services without the need for extensive infrastructure investments, thereby democratizing access to powerful data management tools.
+=== Scalability Issues ===
+While traditional relational databases excel at managing structured data, they may struggle with unstructured or semi-structured data. As a result, some organizations may need to rely on a combination of different database technologies, implementing NoSQL databases for scalability while maintaining relational databases for transactional data.
 == See also ==
-* [[Relational Database Management System]]
+* [[Relational database]]
 * [[NoSQL]]
-* [[Data Warehousing]]
+* [[SQL]]
-* [[Big Data]]
+* [[Database theory]]
-* [[SQL Injection]]
+* [[Big data]]
-* [[Data Integrity]]
+* [[Data warehousing]]
 == References ==
-* [https://www.oracle.com/database/ Oracle Database Official Website]
+* [https://www.oracle.com/database/ Oracle Database Official Site]
-* [https://www.mysql.com/ MySQL Official Website]
+* [https://www.mysql.com/ MySQL Official Site]
-* [https://www.microsoft.com/en-us/sql-server/ Microsoft SQL Server Official Website]
+* [https://www.microsoft.com/en-us/sql-server SQL Server Official Site]
-* [https://www.mongodb.com/ MongoDB Official Website]
+* [https://www.mongodb.com/ MongoDB Official Site]
-* [https://www.postgresql.org/ PostgreSQL Official Website]
-* [https://www.couchbase.com/ Couchbase Official Website]
-* [https://www.db-engines.com/en/ranking_database_management_systems The DB-Engines Ranking of Database Management Systems]
-* [https://www.ibm.com/it-infrastructure/databases/ IBM's Information Management Solutions]
 [[Category:Database management systems]]
-[[Category:Software]]
+[[Category:Software categories]]
-[[Category:Information technology]]
+[[Category:Computer science]]